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Bag-of-Visual Words (BoVW) and deep learning techniques have been widely used in several domains, which include computer-assisted medical diagnoses. In this work, we are interested in developing tools for the automatic identification of Parkinson’s disease using machine learning and the concept of BoVW. The proposed approach concerns a hierarchical-based learning technique to design visual dictionaries through the Deep Optimum-Path Forest classifier. The proposed method was evaluated in six datasets derived from data collected from individuals when performing handwriting exams. Experimental results showed the potential of the technique, with robust achievements.
Parkinson's disease (PD) is a neurodegenerative disease that affects millions of people worldwide, causing mental and mainly motor dysfunctions. The negative impact on the patient's daily routine has moved the science in search of new techniques that can reduce its negative effects and also identify the disease in individuals. One of the main motor characteristics of PD is the hand tremor faced by patients, which turns out to be a crucial information to be used towards a computer-aided diagnosis. In this context, we make use of handwriting dynamics data acquired from individuals when submitted to some tasks that measure abilities related to writing skills. This work proposes the application of recurrence plots to map the signals onto the image domain, which are further used to feed a Convolutional Neural Network for learning proper information that can help the automatic identification of PD. The proposed approach was assessed in a public dataset under several scenarios that comprise different combinations of deep-based architectures, image resolutions, and training set sizes. Experimental results showed significant accuracy improvement compared to our previous work with an average accuracy of over 87%. Moreover, it was observed an improvement in accuracy concerning the classification of patients (i.e., mean recognition rates above to 90%). The promising results showed the potential of the proposed approach towards the automatic identification of Parkinson's disease.
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of exvivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models. However, the limited background variation and simple motion rendered the dataset uninformative in learning about which techniques would be suitable for segmentation in real surgery. In 2017, at the same workshop in Quebec we introduced the robotic instrument segmentation dataset with 10 teams participating in the challenge to perform binary, articulating parts and type segmentation of da Vinci instruments. This challenge included realistic instrument motion and more complex porcine tissue as background and was widely addressed with modfications on U-Nets and other popular CNN architectures [1].
In 2018 we added to the complexity by introducing a set of anatomical objects and medical devices to the segmented classes. To avoid over-complicating the challenge, we continued with porcine data which is dramatically simpler than human tissue due to the lack of fatty tissue occluding many organs.
Background: This preparatory study accelerates an implementation of individualized monitoring and feedback of physical motion using conventional motion trackers in the rehabilitation process of geriatric trauma patients. Regaining mobility is accompanied with improved quality of life in persons of very advanced age recovering from fragility fractures.
Objectives: Quantitative survey of regaining physical mobility provides recommendations for action on how to use motion trackers effectively in a clinical geriatric setting.
Methods: Method mix of quantitative and qualitative interdisciplinary and mutual complementary research approaches (sociology, health research, philosophy/ethics, medical informatics, nursing science, gerontology and physical therapy). While validating motion tracker use in geriatric traumatology preliminary data are used to develop a target group oriented motion feedback. In addition measurement accuracy of a questionnaire about quality of life of multimorbid geriatric patients (FLQM) is tested.
Conclusion: Implementing a new technology in a complex clinical setting needs to be based on a strong theoretical background but will not succeed without careful field testing.
Therapieabbruch oder -unterbrechung erhöhen das Risiko von Krankheitsschüben in der MS-Behandlung (Ennis et al., 2008). Der Abbruchanteil für die immunmodulatorische Basistherapie mit COPAXONE® betrug 2008 in den ersten drei Monaten bis zu 30 % (COPAKTIV Schwestern-Service, 2008), worauf das Aktiv-mit-MS-Patientenbetreuungsprogramm, eine begleitende pflegerische Fachberatung und ein Servicetelefon, installiert wurde. Die Auswirkungen der Zusammenarbeit innerhalb des Teams und mit Ärzt/innen sowie die peer-to-peer Beratung in einem Online-Forum auf Therapietreue und Krankheitsbewältigung der Patient/innen wurde im Längsschnitt untersucht. Ein Methoden-Mix wurde erfolgreich zur Evaluation der pflegerischen Multiprofessionalität eingesetzt. Es zeigte sich, dass die Angebote im Betreuungsprogramm zu einer Verminderung der Abbruchquote führten und die Patient/innen eine gute Therapietreue einhalten.
Hintergrund:
Eltern stehen im Rahmen der eigenen Impfung und der Kinderimpfung mit einem COVID-19-Vakzin vor einer Impfentscheidung. Zum aktuellen Zeitpunkt gibt es keine (vollständige) Impfempfehlung.
Fragestellung:
Die Studie untersucht die Impfbereitschaft von Eltern minderjähriger Kinder und Personen ohne minderjährige Kinder, wobei insbesondere Geschlechtsunterschiede überprüft werden.
Methoden:
Die Studie basiert auf einer Zufallsstichprobe (Telefon-Survey, n = 2014, Erhebung zwischen 12.11.2020 und 10.12.2020). Die Auswertung stützt sich insbesondere auf die Teilstichprobe von Personen mit minderjährigen Kindern im Haushalt (n = 461).
Ergebnisse:
Eltern weisen durchgängig eine geringere Impfbereitschaft mit einem COVID-19-Vakzin auf als Befragte ohne minderjährige Kinder (54,1 % vs. 71,1 %). Väter weisen eine stärker ausgeprägte eigene Impfbereitschaft auf als Mütter. Darüber hinaus sind Männer eher als Frauen bereit, das eigene Kind mit einem COVID-19-Vakzin impfen zu lassen.
Schlussfolgerungen:
Bei Eltern und insbesondere Müttern ist eine erhebliche Fehleinschätzung von Impfrisiken und häufiger Glaube an Impfverschwörungstheorien zu beobachten. Empfohlen werden anschauliche und leicht verständliche Informationen über die Wirkung und Nebenwirkungen der Impfung mit einem COVID-19-Vakzin durch zuständige Institutionen und Ärzte.
Background: This article is based on an ongoing long-term study, in which customary motion trackers measure steps during rehabilitation of geriatric trauma patients (Med=86 years).
Objectives: Exploring steps after 28 days of measurement. Finding similarities in the data by running cluster analysis and formulating linear regressions models to predict steps through time.
Methods: Two types of motion trackers (FitBitAlta HR and Garmin vívofit 3) have been used to measure patients' (N=24) steps after hip fracture in two study groups. Cluster analysis detected three clusters for progress in number of steps that were tested for group differences with ANOVA. Regression analysis tested models for individual patients.
Results: Three-cluster solutions showed significant differences for the average amount of steps after 5, 14, 21 and 28 days. Regression models could predict 71 % of the individual patients' progress in study group 2.
Conclusion: The long-term study will provide more data in the future to examine the three-cluster solution and to find out in what stage of rehabilitation the measurement of the steps could be used to predict individual rehabilitation.
Es handelt sich um eine prospektive Studie zu einem beantragten BMBF-Projekt in einer Kooperation zwischen dem Institut für Sozialforschung und Technikfolgenabschätzung (IST), der Professur für E-Health der Fakultät Informatik und Mathematik an der OTH Regensburg sowie der Alterstraumatologie im Caritas Krankenhaus St. Josef Regensburg. Die geplante Machbarkeitsstudie forciert die Implementierung eines individualisierten Bewegungsmonitorings und feedbacks durch Einsatz eines alltagstauglichen Motion-Trackers in der Rehabilitation geriatrischer Traumapatient/innen. Das Wiedererlangen von Mobilität nach einer Fraktur geht einher mit dem Erhalt der subjektiven Lebensqualität (LQ) hochaltriger Menschen. Durch diagnostische Überwachung mithilfe der Motion-Tracker wird das Wiedererlangen bzw. der Erhalt von Mobilität quantitativ begleitet, um daraus Handlungsempfehlungen für deren nützlichen und kostengünstigen Einsatz in der geriatrischen Rehabilitation abzuleiten. Die Vorstudie dient der Validierung des Einsatzes handelsüblicher Motion-Tracker in der Alterstraumatologie, einschließlich der Entwicklung eines für die Zielgruppe geeigneten Bewegungsfeedbacks sowie der Überprüfung des Fragebogens zur LQ multimorbider älterer Menschen (FLQM). Hierfür werden quantitative und qualitative Methoden der Disziplinen Soziologie, Gesundheitsforschung, Philosophie/Ethik, Datenschutzrecht und medizinische Informatik, Medizin, Pflege, Gerontologie und Physiotherapie, die sich gegenseitig ergänzen (Methoden-Mix), herangezogen.
Nach einer Fraktur ist Mobilisierung Behandlungsziel und Therapiesäule. Das Festlegen von Outcomes basiert jedoch auf vielen Unsicherheiten, da Assessments nicht für alle Patient/-innen geeignet sind. Sie können agesabhängig beeinflusst und subjektiv geprägt sein. Sensorbasiertes Bewegungsmonitoring bietet eine Ergänzung zur Operationalisierung der Gehfähigkeit. Für Längsschnittuntersuchungen, die auch im häuslichen Umfeld durchgeführt werden, eignet sich die tägliche Schrittzahl als Variable. Sie kann durch einen handelsüblichen Fitnesstracker beobachtet
werden.
This article highlights methodological and ethical challenges in research with adults of older and oldest age, by presenting field experiences of the current research project “Motion Monitoring of Geriatric Trauma Patients - Explorative Study on the Rehabilitation Process after Hip Fracture Using Sensor-based Data”. Depiction of the survey situation, with regard to the subjects in particular, can serve as practical examples for designing future research projects.
The group of older adults is a rather large and growing group for which research is required, especially concerning their heterogeneity, their individual autonomy and quality of life. It is assumed, that research designs of studies on the target group must be specifically adjusted, in particular when considering the attribution of vulnerability of the group members. At the same time, it is not clear yet what exact specifics of the subjects and target group must be considered in research designs, as surprisingly little is known about the target group as subjects and corresponding theories have been insufficiently tested.
The exploratory long-term design of the research project presented in the second section of this chapter has a positive evaluation of an ethics committee. Still ethical challenges occurred in the field situation, that are illustrated in the third section of this chapter, by providing information on the patients, their role as research subjects, how they were recruited, how an informed consensus was reached, and in some cases how participation was rejected or abandoned. After a summary, the end of the paper is marked by recommendations on how to design future research projects.
Cumulatively it must always be expected that interaction between researchers and research subjects of this target group can become very intensive, what requires to follow clearly defined procedures and at the same time to be prepared to act flexibly.
Pediatric patients suffering from ependymoma are usually treated with cranial or craniospinal three-dimensional (3D) conformal radiotherapy (3DCRT). Intensity-modulated techniques spare dose to the surrounding tissue, but the risk for second malignancies may be increased due to the increase in low-dose volume. The aim of this study is to investigate if the flattening filter free (FFF) mode allows reducing the risk for second malignancies compared to the mode with flattening filter (FF) for intensity-modulated techniques and to 3DCRT. A reduction of the risk would be advantageous for treating pediatric ependymoma. 3DCRT was compared to intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT) with and without flattening filter. Dose-volume histograms (DVHs) were compared to evaluate the plan quality and used to calculate the excess absolute risk (EAR) to develop second cancer in the brain. Dose verification was performed with a two-dimensional (2D) ionization chamber array and the out-of-field dose was measured with an ionization chamber to determine the EAR in peripheral organs. Delivery times were measured. Both VMAT and IMRT achieved similar plan quality in terms of dose sparing in the OAR and higher PTV coverage as compared to 3DCRT. Peripheral dose in low-dose region, which is proportional to the EAR in organs located in this region, for example, gonads, bladder, or bowel, could be significantly reduced using FFF. The lowest peripheral EAR and lowest delivery times were hereby achieved with VMATFFF . The EAR calculated based on DVH in the brain could not be reduced using FFF mode. VMATFFF improved the target coverage and homogeneity and kept the dose in the OAR similar compared to 3DCRT. In addition, delivery times were significantly reduced using VMATFFF . Therefore, for radiotherapy of ependymoma patients, VMATFFF may be considered advantageous for the combination of Elekta Synergy linac and Oncentra External Beam planning system used in this study.
Objective: Artificial intelligence (AI) may reduce underdiagnosed or overlooked upper GI (UGI) neoplastic and preneoplastic conditions, due to subtle appearance and low disease prevalence. Only disease-specific AI performances have been reported, generating uncertainty on its clinical value.
Design: We searched PubMed, Embase and Scopus until July 2020, for studies on the diagnostic performance of AI in detection and characterisation of UGI lesions. Primary outcomes were pooled diagnostic accuracy, sensitivity and specificity of AI. Secondary outcomes were pooled positive (PPV) and negative (NPV) predictive values. We calculated pooled proportion rates (%), designed summary receiving operating characteristic curves with respective area under the curves (AUCs) and performed metaregression and sensitivity analysis.
Results: Overall, 19 studies on detection of oesophageal squamous cell neoplasia (ESCN) or Barrett's esophagus-related neoplasia (BERN) or gastric adenocarcinoma (GCA) were included with 218, 445, 453 patients and 7976, 2340, 13 562 images, respectively. AI-sensitivity/specificity/PPV/NPV/positive likelihood ratio/negative likelihood ratio for UGI neoplasia detection were 90% (CI 85% to 94%)/89% (CI 85% to 92%)/87% (CI 83% to 91%)/91% (CI 87% to 94%)/8.2 (CI 5.7 to 11.7)/0.111 (CI 0.071 to 0.175), respectively, with an overall AUC of 0.95 (CI 0.93 to 0.97). No difference in AI performance across ESCN, BERN and GCA was found, AUC being 0.94 (CI 0.52 to 0.99), 0.96 (CI 0.95 to 0.98), 0.93 (CI 0.83 to 0.99), respectively. Overall, study quality was low, with high risk of selection bias. No significant publication bias was found.
Conclusion: We found a high overall AI accuracy for the diagnosis of any neoplastic lesion of the UGI tract that was independent of the underlying condition. This may be expected to substantially reduce the miss rate of precancerous lesions and early cancer when implemented in clinical practice.
Signal transmission between different brain regions requires connecting fiber tracts, the structural basis of the human connectome. In contrast to animal brains, where a multitude of tract tracing methods can be used, magnetic resonance (MR)-based diffusion imaging is presently the only promising approach to study fiber tracts between specific human brain regions. However, this procedure has various inherent restrictions caused by its relatively low spatial resolution. Here, we introduce 3D-polarized light imaging (3D-PLI) to map the three-dimensional course of fiber tracts in the human brain with a resolution at a submillimeter scale based on a voxel size of 100 μm isotropic or less. 3D-PLI demonstrates nerve fibers by utilizing their intrinsic birefringence of myelin sheaths surrounding axons. This optical method enables the demonstration of 3D fiber orientations in serial microtome sections of entire human brains. Examples for the feasibility of this novel approach are given here. 3D-PLI enables the study of brain regions of intense fiber crossing in unprecedented detail, and provides an independent evaluation of fiber tracts derived from diffusion imaging data.
The German National Educational Panel Study (NEPS) was set up to provide an empirical basis for longitudinal analyses of individuals’ educational careers and competencies and how they unfold over the life course in relation to family, formal educational institutions, and private life. Educational developments and decisions over the life span are being tracked in six starting cohorts as a foundation for characterizing and analyzing educational processes. These six starting cohorts include newborns, Kindergarten children, secondary school children (5th and 9th grade), first-year undergraduate students, and adults. Because access to the target population in several starting cohorts was gained via educational institutions such as Kindergartens and schools, multistage sampling approaches were implemented that reflect the clustered structure of the target populations. Samples in individual contexts, such as those in the adult and newborn cohorts, were established via register-based stratified cluster approaches. This chapter briefly reviews the designs of the implemented sampling strategies for each established starting cohort and provides information on the levels of attrition in the panel development.
Background and aims:
Rapid pre-hospital identification of stroke symptoms result in therapy directed admission to dedicated stroke units. Widespread application of stroke scales reveal high sensitivity but low specificity, especially in non-academic first aid personal. In our previous work we show that prehospital stroke diagnostics based on neurolo-gical examination and transcranial color-coded Duplex so-nography (TCCS) is feasible and results in high sensitivity and specificity for middle cerebral artery / distal internal carotid artery occlusion. The aim of our ongoing study is to design and evaluate a dedicated stroke educational program for paramedics including transcranial ultrasound. This is a prerequisite for a telemedical decision support system in the absence of stroke experienced emergency doctors.
Methods:
We currently educate 6 paramedics in advanced stroke neurology and also transcranial ultrasound examina-tion during a course of 2 months. The web-based curricu-lum was designed in two parts. The first was theoretical and the second will be the real-life training under neurological supervision. For final assessment of the theoretical know-ledge a control group will be implemented without specific stroke expertise. The stroke-educated paramedics will have to assist stroke investigation, perform pre-hospital TCCS and enter the date in a mobile telestroke pad. The data set will be send to an in-hospital stroke physician. We will as-sess the mean time to reach the diagnostic assessment, its sensitivity and specificity and the patient outcome after 30 days.
Results:
Our study is ongoing
Conclusion:
The study just reach the 2 part and we will be glad to present our data on the meeting EAN 2015.
Disclosure:
Nothing to disclose
Although it has long been presumed that d-amino acids are uncommon in mammalians, substantial amounts of free d-serine have been detected in the mammalian brain. d-Serine has been demonstrated to be an important modulator of glutamatergic neurotransmission and acts as an agonist at the strychnine-insensitive glycine site of N-methyl-d-aspartate receptors. The blood-to-brain transfer of d-serine is thought to be extremely low, and it is assumed that d-serine is generated by isomerization of l-serine in the brain. Stimulated by the observation of a preferred transport of the d-isomer of proline at the blood–brain barrier, we investigated the differential uptake of [3H]-d-serine and [3H]-l-serine in the rat brain 1 h after intravenous injection using quantitative autoradiography. Surprisingly, brain uptake of [3H]-d-serine was significantly higher than that of [3H]-l-serine, indicating a preferred transport of the d-enantiomer of serine at the blood–brain barrier. This finding indicates that exogenous d-serine may have a direct influence on glutamatergic neurotransmission and associated diseases.
Objectives: C-11-methionine (MET) is particularly useful in brain tumor diagnosis but unspecific uptake e.g. in cerebral ischemia has been reported (1). The F-18-labeled amino acid O-(2-[F-18]fluoroethyl)-L-tyrosine (FET) shows a similar clinical potential as MET in brain tumor diagnosis but is applicable on a wider clinical scale. The aim of this study was to evaluate the uptake of FET and H-3-MET in focal cortical ischemia in rats by dual tracer autoradiography.
Methods: Focal cortical ischemia was induced in 12 Fisher CDF rats using the photothrombosis model (PT). One day (n=3) , two days (n=5) and 7 days (n=4) after induction of the lesion FET and H-3-MET were injected intravenously. One hour after tracer injection animals were killed, the brains were removed immediately and frozen in 2-methylbutane at -50°C. Brains were cut in coronal sections (thickness: 20 µm) and exposed first to H-3 insensitive photoimager plates to measure FET distribution. After decay of F-18 the distribution of H-3-MET was determined. The autoradiograms were evaluated by regions of interest (ROIs) placed on areas with increased tracer uptake in the PT and the contralateral brain. Lesion to brain ratios (L/B) were calculated by dividing the mean uptake in the lesion and the brain. Based on previous studies in gliomas a L/B ratio > 1.6 was considered as pathological for FET.
Results: Variable increased uptake of both tracers was observed in the PT and its demarcation zone at all stages after PT. The cut-off level of 1.6 for FET was exceeded in 9/12 animals. One day after PT the L/B ratios were 2.0 ± 0.6 for FET vs. 2.1 ± 1.0 for MET (mean ± SD); two days after lesion 2.2 ± 0.7 for FET vs. 2.7 ± 1.0 for MET and 7 days after lesion 2.4 ± 0.4 for FET vs. 2.4 ± 0.1 for MET. In single cases discrepancies in the uptake pattern of FET and MET were observed.
Conclusions: FET like MET may exhibit significant uptake in infarcted areas or the immediate vincinity which has to be considered in the differential diagnosis of unkown brain lesions. The discrepancies in the uptake pattern of FET and MET in some cases indicates either differences in the transport mechanisms of both amino acids or a different affinity for certain cellular components.
Mass spectrometric imaging (MSI) is a young innovative analytical technique and combines different fields of advanced mass spectrometry and biomedical research with the aim to provide maps of elements and molecules, complexes or fragments. Especially essential metals such as zinc, copper, iron and manganese play a functional role in signaling, metabolism and homeostasis of the cell. Due to the high degree of spatial organization of metals in biological systems their distribution analysis is of key interest in life sciences. We have developed analytical techniques termed BrainMet using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) imaging to measure the distribution of trace metals in biological tissues for biomedical research and feasibility studies—including bioaccumulation and bioavailability studies, ecological risk assessment and toxicity studies in humans and other organisms. The analytical BrainMet techniques provide quantitative images of metal distributions in brain tissue slices which can be combined with other imaging modalities such as photomicrography of native or processed tissue (histochemistry, immunostaining) and autoradiography or with in vivo techniques such as positron emission tomography or magnetic resonance tomography.
Prospective and instrumental developments will be discussed concerning the development of the metalloprotein microscopy using a laser microdissection (LMD) apparatus for specific sample introduction into an inductively coupled plasma mass spectrometer (LMD-ICP-MS) or an application of the near field effect in LA-ICP-MS (NF-LA-ICP-MS). These nano-scale mass spectrometric techniques provide improved spatial resolution down to the single cell level.
Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has been developed and established as an emerging technique in the generation of quantitative images of metal distributions in thin tissue sections of brain samples (such as human, rat and mouse brain), with applications in research related to neurodegenerative disorders. A new analytical protocol is described which includes sample preparation by cryo-cutting of thin tissue sections and matrix-matched laboratory standards, mass spectrometric measurements, data acquisition, and quantitative analysis. Specific examples of the bioimaging of metal distributions in normal rodent brains are provided. Differences to the normal were assessed in a Parkinson’s disease and a stroke brain model. Furthermore, changes during normal aging were studied. Powerful analytical techniques are also required for the determination and characterization of metal-containing proteins within a large pool of proteins, e.g., after denaturing or non-denaturing electrophoretic separation of proteins in one-dimensional and two-dimensional gels. LA-ICP-MS can be employed to detect metalloproteins in protein bands or spots separated after gel electrophoresis. MALDI-MS can then be used to identify specific metal-containing proteins in these bands or spots. The combination of these techniques is described in the second section.
Bioimaging of Metals by Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS)
(2010)
The distribution analysis of (essential, beneficial, or toxic) metals (e.g., Cu, Fe, Zn, Pb, and others), metalloids, and non‐metals in biological tissues is of key interest in life science. Over the past few years, the development and application of several imaging mass spectrometric techniques has been rapidly growing in biology and medicine. Especially, in brain research metalloproteins are in the focus of targeted therapy approaches of neurodegenerative diseases such as Alzheimer's and Parkinson's disease, or stroke, or tumor growth. Laser ablation inductively coupled plasma mass spectrometry (LA‐ICP‐MS) using double‐focusing sector field (LA‐ICP‐SFMS) or quadrupole‐based mass spectrometers (LA‐ICP‐QMS) has been successfully applied as a powerful imaging (mapping) technique to produce quantitative images of detailed regionally specific element distributions in thin tissue sections of human or rodent brain. Imaging LA‐ICP‐QMS was also applied to investigate metal distributions in plant and animal sections to study, for example, the uptake and transport of nutrient and toxic elements or environmental contamination. The combination of imaging LA‐ICP‐MS of metals with proteomic studies using biomolecular mass spectrometry identifies metal‐containing proteins and also phosphoproteins. Metal‐containing proteins were imaged in a two‐dimensional gel after electrophoretic separation of proteins (SDS or Blue Native PAGE). Recent progress in LA‐ICP‐MS imaging as a stand‐alone technique and in combination with MALDI/ESI‐MS for selected life science applications is summarized.
Purpose
MR-based attenuation correction (AC) will become an integral part of combined PET/MR systems. Here, we propose a toolbox to validate MR-AC of clinical PET/MRI data sets.
Methods
Torso scans of ten patients were acquired on a combined PET/CT and on a 1.5-T MRI system. MR-based attenuation data were derived from the CT following MR–CT image co-registration and subsequent histogram matching. PET images were reconstructed after CT- (PET/CT) and MR-based AC (PET/MRI). Lesion-to-background (L/B) ratios were estimated on PET/CT and PET/MRI.
Results
MR–CT histogram matching leads to a mean voxel intensity difference in the CT- and MR-based attenuation images of 12% (max). Mean differences between PET/MRI and PET/CT were 19% (max). L/B ratios were similar except for the lung where local misregistration and intensity transformation leads to a biased PET/MRI.
Conclusion
Our toolbox can be used to study pitfalls in MR-AC. We found that co-registration accuracy and pixel value transformation determine the accuracy of PET/MRI.
Ausgehend von einer Betrachtung des Einflusses von InformatikerInnen und ihren Handlungsmustern auf Arbeitsprozesse und vor dem Hintergrund einer kritischen Auseinandersetzung mit bisherigen berufsethischen Leitlinien wollen wir einen neuen Ansatz wagen, über Informatik und Verantwortung nachzudenken. Eine Rekonstruktion des Begriffs Verantwortung bietet uns ein reichhaltiges begriffliches Instrumentarium zur Reflexion des Verhältnisses von Verantwortung und informatischer Praxis. Wir plädieren dafür, dass informatisch Handelnde lernen müssen, ethische Probleme zu erkennen, implizite Annahmen von Arbeitskulturen zu durchschauen und ein Bewusstsein für die Veränderbarkeit dieser Arbeitskulturen entwickeln sollten. Zusätzlich bedarf es kollektiver und rechtlicher Veränderungen der Rahmenbedingungen für verantwortliches Handeln.
Reduction of variable-truncation artifacts from beam occlusion during in situ x-ray tomography
(2017)
Many in situ x-ray tomography studies require experimental rigs which may partially occlude the beam and cause parts of the projection data to be missing. In a study of fluid flow in porous chalk using a percolation cell with four metal bars drastic streak artifacts arise in the filtered backprojection (FBP) reconstruction at certain orientations. Projections with non-trivial variable truncation caused by the metal bars are the source of these variable-truncation artifacts. To understand the artifacts a mathematical model of variable-truncation data as a function of metal bar radius and distance to sample is derived and verified numerically and with experimental data. The model accurately describes the arising variable-truncation artifacts across simulated variations of the experimental setup. Three variable-truncation artifact-reduction methods are proposed, all aimed at addressing sinogram discontinuities that are shown to be the source of the streaks. The 'reduction to limited angle' (RLA) method simply keeps only non-truncated projections; the 'detector-directed smoothing' (DDS) method smooths the discontinuities; while the 'reflexive boundary condition' (RBC) method enforces a zero derivative at the discontinuities. Experimental results using both simulated and real data show that the proposed methods effectively reduce variable- truncation artifacts. The RBC method is found to provide the best artifact reduction and preservation of image features using both visual and quantitative assessment. The analysis and artifact-reduction methods are designed in context of FBP reconstruction motivated by computational efficiency practical for large, real synchrotron data. While a specific variable- truncation case is considered, the proposed methods can be applied to general data cut-offs arising in different in situ x-ray tomography experiments.
Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.
Aims
Congenital breast asymmetry represents a particular challenge to the classic techniques of plastic surgery given the young age of patients at presentation. This study reviews and compares the long-term results of traditional breast augmentation using silicone implants and the more innovative technique of lipografting.
Methods
To achieve this, we not only captured subjective parameters such as satisfaction with outcome and symmetry, but also objective parameters including breast vol-ume and anthropometric measurements. The objective examination was performed manually and by using the Vectra H2 photogrammetry scanning system.
Results
Differences between patients undergoing either implant augmentation or lipograft were revealed not to be significant with respect to patient satisfaction with surgical outcome (p= 0.55) and symmetry (p= 0.69). Furthermore, a breast symmetry of 93 % was reported in both groups. Likewise, no statistically significant volume difference between the left and right breasts was observed in both groups (p\0.41). However, lipograft patients needed on average 2.9 procedures to achieve the desired result, compared with 1.3 for implant augmentation. In contrast, patients treated with implant augmentation may require anumber of implant changes during their lifetime.
Conclusion
Both methods may be considered for patients presenting with congenital breast asymmetry.
Chest X-Ray (CXR) images as part of a non-invasive diagnosis method are commonly used in today’s medical workflow. In traditional methods, physicians usually use their experience to interpret CXR images, however, there is a large interobserver variance. Computer vision may be used as a standard for assisted diagnosis. In this study, we applied an encoder-decoder neural network architecture for automatic lung region detection. We compared a three-class approach (left lung, right lung, background) and a two-class approach (lung, background). The differentiation of left and right lungs as direct result of a semantic segmentation on basis of neural nets rather than post-processing a lung-background segmentation is done here for the first time. Our evaluation was done on the NIH Chest X-ray dataset, from which 1736 images were extracted and manually annotated. We achieved 94:9% mIoU and 92% mIoU as segmentation quality measures for the two-class-model and the three-class-model, respectively. This result is very promising for the segmentation of lung regions having the simultaneous classification of left and right lung in mind.
Cybersecurity is of capital importance in a world where economic and social processes increasingly rely on digital technology. Although the primary ethical motivation of cybersecurity is prevention of informational or physical harm, its enforcement can also entail conflicts with other moral values. This contribution provides an outline of value conflicts in cybersecurity based on a quantitative literature analysis and qualitative case studies. The aim is to demonstrate that the security-privacy-dichotomy—that still seems to dominate the ethics discourse based on our bibliometric analysis—is insufficient when discussing the ethical challenges of cybersecurity.
Furthermore, we want to sketch how the notion of contextual integrity could help to better understand and mitigate such value conflicts.
Das vorliegende Lehr- und Arbeitsbuch, das im Wesentlichen die mathematische Grundausbildung im ersten Studienjahr technisch- und naturwissenschaftlich orientierter Studiengänge abdeckt, verfolgt zwei Ziele: Zum einen sollen praxisrelevante mathematische Methoden eingeübt werden, wie es etwa in ingenieurwissenschaftlichen Studiengängen gefordert wird. Zum anderen sollen Studierende die Fähigkeit erwerben, sich selbstständig in unbekannte mathematische Texte einzuarbeiten. Letzteres ist notwendig, weil eine mathematische Grundausbildung niemals alle Themen abdecken kann, die im weiteren Studium oder später im Arbeitsleben möglicherweise gebraucht werden. Hier würden auswendig gelernte Kochrezepte scheitern – stattdessen sollten Zusammenhänge erkannt werden. Das vorliegende Buch erleichtert dies durch einen formal-theoretischen Rahmen sowie einen logisch konsistenten Aufbau inklusive ausführlicher Beweise für fast alle Behauptungen. Praxisbezüge werden insbesondere hergestellt zur Physik, Signal- und Systemtheorie sowie Energietechnik. Weitere didaktische Besonderheiten des Buches: Im Text sind kleine Übungen und Verständnisfragen eingestreut, die den Leser beim Durcharbeiten des Stoffes zum aktiven Mitmachen anregen. Nach jedem Abschnitt werden außerdem Übungsaufgaben angeboten, die in Rechen- und Theorieaufgaben unterteilt sind. Erstere dienen dem Anwenden und Einüben der wesentlichen Rechenmethoden, letztere dem Verstehen und Erkennen von Zusammenhängen. Über 250 Abbildungen und Diagramme stärken die Anschauungskraft des Lesers.
Polarized light imaging (PLI) enables the evaluation of fiber orientations in histological sections of human postmortem brains, with ultra-high spatial resolution. PLI is based on the birefringent properties of the myelin sheath of nerve fibers. As a result, the polarization state of light propagating through a rotating polarimeter is changed in such a way that the detected signal at each measurement unit of a charged-coupled device (CCD) camera describes a sinusoidal signal. Vectors of the fiber orientation defined by inclination and direction angles can then directly be derived from the optical signals employing PLI analysis. However, noise, light scatter and filter inhomogeneities interfere with the original sinusoidal PLI signals. We here introduce a novel method using independent component analysis (ICA) to decompose the PLI images into statistically independent component maps. After decomposition, gray and white matter structures can clearly be distinguished from noise and other artifacts. The signal enhancement after artifact rejection is quantitatively evaluated in 134 histological whole brain sections. Thus, the primary sinusoidal signals from polarized light imaging can be effectively restored after noise and artifact rejection utilizing ICA. Our method therefore contributes to the analysis of nerve fiber orientation in the human brain within a micrometer scale.
Computer-assisted analysis of endoscopic images can be helpful to the automatic diagnosis and classification of neoplastic lesions. Barrett's esophagus (BE) is a common type of reflux that is not straight forward to be detected by endoscopic surveillance, thus being way susceptible to erroneous diagnosis, which can cause cancer when not treated properly. In this work, we introduce the Optimum-Path Forest (OPF) classifier to the task of automatic identification of Barrett'sesophagus, with promising results and outperforming the well known Support Vector Machines (SVM) in the aforementioned context. We consider describing endoscopic images by means of feature extractors based on key point information, such as the Speeded up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT), for further designing a bag-of-visual-wordsthat is used to feed both OPF and SVM classifiers. The best results were obtained by means of the OPF classifier for both feature extractors, with values lying on 0.732 (SURF) - 0.735(SIFT) for sensitivity, 0.782 (SURF) - 0.806 (SIFT) for specificity, and 0.738 (SURF) - 0.732 (SIFT) for the accuracy.
In this work, we propose the use of single channel Color Co-occurrence Matrices for texture description of Barrett’sEsophagus (BE)and adenocarcinoma images. Further classification using supervised learning techniques, such as Optimum-Path Forest (OPF), Support Vector Machines with Radial Basisunction (SVM-RBF) and Bayesian classifier supports the contextof automatic BE and adenocarcinoma diagnosis. We validated three approaches of classification based on patches, patients and images in two datasets (MICCAI 2015 and Augsburg) using the color-and-texture descriptors and the machine learning techniques. Concerning MICCAI 2015 dataset, the best results were obtained using the blue channel for the descriptors and the supervised OPF for classification purposes in the patch-based approach, with sensitivity nearly to 73% for positive adenocarcinoma identification and specificity close to 77% for BE (non-cancerous) patch classification. Regarding the Augsburg dataset, the most accurate results were also obtained using both OPF classifier and blue channel descriptor for the feature extraction, with sensitivity close to 67% and specificity around to76%. Our work highlights new advances in the related research area and provides a promising technique that combines color and texture information, allied to three different approaches of dataset pre-processing aiming to configure robust scenarios for the classification step.
The development of adenocarcinoma in Barrett’s esophagus is difficult to detect by endoscopic surveillance of patients with signs of dysplasia. Computer assisted diagnosis of endoscopic images (CAD) could therefore be most helpful in the demarcation and classification of neoplastic lesions. In this study we tested the feasibility of a CAD method based on Speeded up Robust Feature Detection (SURF). A given database containing 100 images from 39 patients served as benchmark for feature based classification models. Half of the images had previously been diagnosed by five clinical experts as being ”cancerous”, the other half as ”non-cancerous”. Cancerous image regions had been visibly delineated (masked) by the clinicians. SURF features acquired from full images as well as from masked areas were utilized for the supervised training and testing of an SVM classifier. The predictive accuracy of the developed CAD system is illustrated by sensitivity and specificity values. The results based on full image matching where 0.78 (sensitivity) and 0.82 (specificity) were achieved, while the masked region approach generated results of 0.90 and 0.95, respectively.
Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their level of accountability and transparency must be provided in such evaluations. The reliability related to machine learning predictions must be explained and interpreted, especially if diagnosis support is addressed. For this task, the black-box nature of deep learning techniques must be lightened up to transfer its promising results into clinical practice. Hence, we aim to investigate the use of explainable artificial intelligence techniques to quantitatively highlight discriminative regions during the classification of earlycancerous tissues in Barrett’s esophagus-diagnosed patients. Four Convolutional Neural Network models (AlexNet, SqueezeNet, ResNet50, and VGG16) were analyzed using five different interpretation techniques (saliency, guided backpropagation, integrated gradients, input × gradients, and DeepLIFT) to compare their agreement with experts’ previous annotations of cancerous tissue. We could show that saliency attributes match best with the manual experts’ delineations. Moreover, there is moderate to high correlation between the sensitivity of a model and the human-and-computer agreement. The results also lightened that the higher the model’s sensitivity, the stronger the correlation of human and computational segmentation agreement. We observed a relevant relation between computational learning and experts’ insights, demonstrating how human knowledge may influence the correct computational learning.
This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. We compile some works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer, and Hindawi Publishing Corporation. Each selected work has been analyzed to present its objective, methodology, and results. The BE progression to dysplasia or adenocarcinoma shows a complex pattern to be detected during endoscopic surveillance. Therefore, it is valuable to assist its diagnosis and automatic identification using computer analysis. The evaluation of the BE dysplasia can be performed through manual or automated segmentation through machine learning techniques. Finally, in this survey, we reviewed recent studies focused on the automatic detection of the neoplastic region for classification purposes using machine learning methods.
Barrett's esophagus denotes a disorder in the digestive system that affects the esophagus' mucosal cells, causing reflux, and showing potential convergence to esophageal adenocarcinoma if not treated in initial stages. Thus, fast and reliable computer-aided diagnosis becomes considerably welcome. Nevertheless, such approaches usually suffer from imbalanced datasets, which can be addressed through Generative Adversarial Networks (GANs). Such techniques generate realistic images based on observed samples, even though at the cost of a proper selection of its hyperparameters. Many works employed a class of nature-inspired algorithms called metaheuristics to tackle the problem considering distinct deep learning approaches. Therefore, this paper's main contribution is to introduce metaheuristic techniques to fine-tune GANs in the context of Barrett's esophagus identification, as well as to investigate the feasibility of generating high-quality synthetic images for early-cancer assisted identification.
Quantification of growth in experimental F98 and C6 rat brain tumours was performed on 51 rat brains, 17 of which have been further assessed by 3D tumour reconstruction. Brains were cryosliced and radio-labelled with a ligand of the peripheral type benzodiazepine-receptor (pBR), 3H-Pk11195 [(1-(2-chlorophenyl)-N-methyl-N-(1-methyl-propylene)-3-isoquinoline-carboxamide)] by receptor autoradiography. Manually segmented and automatically registered tumours have been 3D-reconstructed for volumetric comparison on the basis of 3H-Pk11195-based tumour recognition. Furthermore automatically computed areas of −300 μm inner (marginal) zone as well as 300 μm and 600 μm outer tumour space were quantified. These three different regions were transferred onto other adjacent slices that had been labelled by receptor autoradiography with the A1 Adenosine receptor (A1AR)-ligand 3H-CPFPX (3H-8-cyclopentyl-3-(3-fluorpropyl)-1-propylxanthine) for quantitative assessment of A1AR in the three different tumour zones. Hence, a method is described for quantifying various receptor protein systems in the tumour as well as in the marginal invasive zones around experimentally implanted rat brain tumours and their representation in the tumour microenvironment as well as in 3D space. Furthermore, a tool for automatically reading out radio-labelled rat brain slices from auto radiographic films was developed, reconstructed into a consistent 3D-tumour model and the zones around the tumour were visualized. A1AR expression was found to depend upon the tumour volume in C6 animals, but is independent on the time of tumour development. In F98 animals, a significant increase in A1AR receptor protein was found in the Peritumoural zone as a function of time of tumour development and tumour volume.
Medical image processing provides core innovation for medical imaging. This paper is focused on recent developments from science to applications analyzing the past fifteen years of history of the proceedings of the German annual meeting on medical image processing (BVM). Furthermore, some members of the program committee present their personal points of views: (i) multi-modality for imaging and diagnosis, (ii) analysis of diffusion-weighted imaging, (iii) model-based image analysis, (iv) registration of section images, (v) from images to information in digital endoscopy, and (vi) virtual reality and robotics. Medical imaging and medical image computing is seen as field of rapid development with clear trends to integrated applications in diagnostics, treatment planning and treatment.
Background
The aim of this study was to investigate if the flattening filter free mode (FFF) of a linear accelerator reduces the excess absolute risk (EAR) for second cancer as compared to the flat beam mode (FF) in simultaneous integrated boost (SIB) radiation therapy of right-sided breast cancer.
Patients and methods
Six plans were generated treating the whole breast to 50.4 Gy and a SIB volume to 63 Gy on CT data of 10 patients: intensity-modulated radiation therapy (IMRT), volumetric modulated arc therapy (VMAT), and a tangential arc VMAT (tVMAT), each with flattening filter and without. The EAR was calculated for the contralateral breast and the lungs from dose-volume histograms (DVH) based on the linear-exponential, the plateau, and the full mechanistic dose-response model. Peripheral low-dose measurements were performed to compare the EAR in more distant regions as the thyroids and the uterus.
Results
FFF reduces the EAR significantly in the contralateral and peripheral organs for tVMAT and in the peripheral organs for VMAT. No reduction was found for IMRT. The lowest EAR for the contralateral breast and lung was achieved with tVMAT FFF, reducing the EAR by 25 % and 29 % as compared to tVMAT FF, and by 44 % to 58 % as compared to VMAT and IMRT in both irradiation modes. tVMAT FFF showed also the lowest peripheral dose corresponding to the lowest EAR in the thyroids and the uterus.
Conclusion
The use of FFF mode allows reducing the EAR significantly when tVMAT is used as the treatment technique. When second cancer risk is a major concern, tVMAT FFF is considered the preferred treatment option in SIB irradiation of right-sided breast cancer.
Gesundheit und Soziales
(2023)
Mobiltelefone zeigen weitreichende Auswirkungen auf interpersonale Beziehungen. Die prekäre Balance zwischen Vertrauen als wichtige Basis interpersonaler Beziehungen und Kontrolle als notwendige Be-dingung von Gemeinschaft sowie Privatsphäre als (sozialer) Raum, in dem soziale Kontrolle temporär suspendiert ist, wird durch moderne Informations-und Kommunikationstechnologien gefährdet. Theoreti-sche Konzepte und empirische Studien werden im Beitrag genutzt, um ein besserers Verständnis der Auswirkungen zu ermöglichen.
Hintergrund
Das Internet im Allgemeinen und digitale soziale Netzwerke im Speziellen entwickeln sich zunehmend zu wichtigen Informationsquellen für Gesundheitsinformationen; dies gilt auch für Informationen im Kontext der Behandlung von Kinderwunschpatienten. Erste Studien zeigen, dass die Betroffenen nicht nur nach sachlichen Informationen, sondern auch nach emotionalem Beistand suchen. Ob insbesondere digitale soziale Netzwerke in Ergänzung oder in Konkurrenz zu klassischen Informations- und Beratungsangeboten stehen, ist noch unklar.
Methode
Um eine vorläufige Antwort hierauf geben zu können, wurde eine Literaturstudie erstellt, mit der die wichtigsten empirischen Aussagen bzgl. der Nutzungsweisen von Internet und digitalen sozialen Netzwerken eruiert werden sollten. Dies wurde mithilfe einer Keyword-Suche in Literaturdatenbanken und anschließendem Schlagwort-Snowballing umgesetzt.
Ergebnisse
Informationen und Beratung aus dem Internet und aus digitalen sozialen Netzwerken können als Ergänzung der ärztlichen Expertise angesehen werden, stellen aber keinen Ersatz dar. Insbesondere digitale soziale Netzwerke ergänzen soziale Nahbeziehungen und das Verhältnis zum behandelnden Arzt und werden in Zukunft noch an Wichtigkeit zunehmen.
Schlussfolgerung
Für die professionell Tätigen im Bereich der Reproduktionsmedizin könnte dies ein Hinweis auf Defizite in Bezug auf Beratung und Behandlung sein, dem in Zukunft durch eigene neue Angebote begegnet werden sollte.
Background. The Internet in general and digital social networks in particular are increasingly important sources for health information; this also applies to information in the context of treatment of patients wishing for a baby. Initial studies show that patients are not only looking for factual information but also for emotional support. Whether digital social networks add to or compete with classical information and consulting is still unclear.
Methods. In order to provide a preliminary answer to this question, a literature study was conducted to determine relevant empirical findings regarding the use of the Internet and digital social networks. This was done using a keyword search in literature databases and then keyword snowballing.
Results. Information and advice from the Internet and from digital social networks can be seen as an addition to medical expertise, but are not a substitute. Digital social networks, in particular, supplement social peer groups and the relationship with physicians and will become even more important in the future.
Conclusion. For professionals active in the field of reproductive medicine, this could be an indication of deficits with regard to counseling and treatment, which in the future might be countered by providing new services.
Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma
(2019)
Computer-aided diagnosis using deep learning (CAD-DL) may be an instrument to improve endoscopic assessment of Barrett’s oesophagus
(BE) and early oesophageal adenocarcinoma (EAC). Based on still images from two databases, the diagnosis of EAC by CAD-DL reached sensitivities/specificities of 97%/88% (Augsburg data) and 92%/100% (Medical Image Computing and Computer-Assisted Intervention [MICCAI]
data) for white light (WL) images and 94%/80% for narrow band images (NBI) (Augsburg data), respectively. Tumour margins delineated by
experts into images were detected satisfactorily with a Dice coefficient (D) of 0.72. This could be a first step towards CAD-DL for BE assessment. If developed further, it could become a useful
adjunctive tool for patient management.
Aims:
The delineation of outer margins of early Barrett's cancer can be challenging even for experienced endoscopists. Artificial intelligence (AI) could assist endoscopists faced with this task. As of date, there is very limited experience in this domain. In this study, we demonstrate the measure of overlap (Dice coefficient = D) between highly experienced Barrett endoscopists and an AI system in the delineation of cancer margins (segmentation task).
Methods:
An AI system with a deep convolutional neural network (CNN) was trained and tested on high-definition endoscopic images of early Barrett's cancer (n = 33) and normal Barrett's mucosa (n = 41). The reference standard for the segmentation task were the manual delineations of tumor margins by three highly experienced Barrett endoscopists. Training of the AI system included patch generation, patch augmentation and adjustment of the CNN weights. Then, the segmentation results from patch classification and thresholding of the class probabilities. Segmentation results were evaluated using the Dice coefficient (D).
Results:
The Dice coefficient (D) which can range between 0 (no overlap) and 1 (complete overlap) was computed only for images correctly classified by the AI-system as cancerous. At a threshold of t = 0.5, a mean value of D = 0.72 was computed.
Conclusions:
AI with CNN performed reasonably well in the segmentation of the tumor region in Barrett's cancer, at least when compared with expert Barrett's endoscopists. AI holds a lot of promise as a tool for better visualization of tumor margins but may need further improvement and enhancement especially in real-time settings.
Based on previous work by our group with manual annotation of visible Barrett oesophagus (BE) cancer images, a real-time deep learning artificial intelligence (AI) system was developed. While an expert endoscopist conducts the endoscopic assessment of BE, our AI system captures random images from the real-time camera livestream and provides a global prediction (classification), as well as a dense prediction (segmentation) differentiating accurately between normal BE and early oesophageal adenocarcinoma (EAC). The AI system showed an accuracy of 89.9% on 14 cases with neoplastic BE.
The early diagnosis of cancer in Barrett’s esophagus is crucial for improving the prognosis. However, identifying Barrett’s esophagus-related neoplasia (BERN) is challenging, even for experts [1]. Four-quadrant biopsies may improve the detection of neoplasia, but they can be associated with sampling errors. The application of artificial intelligence (AI) to the assessment of Barrett’s esophagus could improve the diagnosis of BERN, and this has been demonstrated in both preclinical and clinical studies [2] [3].
In this video demonstration, we show the accurate detection and delineation of BERN in two patients ([Video 1]). In part 1, the AI system detects a mucosal cancer about 20 mm in size and accurately delineates the lesion in both white-light and narrow-band imaging. In part 2, a small island of BERN with high-grade dysplasia is detected and delineated in white-light, narrow-band, and texture and color enhancement imaging. The video shows the results using a transparent overlay of the mucosal cancer in real time as well as a full segmentation preview. Additionally, the optical flow allows for the assessment of endoscope movement, something which is inversely related to the reliability of the AI prediction. We demonstrate that multimodal imaging can be applied to the AI-assisted detection and segmentation of even small focal lesions in real time.
Background and aims: The accurate differentiation between T1a and T1b Barrett’s cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett’s cancer white-light images.
Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett’s cancer.
Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively.
Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett’s cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI.
In this study, we aimed to develop an artificial intelligence clinical decision support solution to mitigate operator-dependent limitations during complex endoscopic procedures such as endoscopic submucosal dissection and peroral endoscopic myotomy, for example, bleeding and perforation. A DeepLabv3-based model was trained to delineate vessels, tissue structures and instruments on endoscopic still images from such procedures. The mean cross-validated Intersection over Union and Dice Score were 63% and 76%, respectively. Applied to standardised video clips from third-space endoscopic procedures, the algorithm showed a mean vessel detection rate of 85% with a false-positive rate of 0.75/min. These performance statistics suggest a potential clinical benefit for procedure safety, time and also training.
Introduction
We present a clinical case showing the real-time detection, characterization and delineation of an early Barrett’s cancer using AI.
Patients and methods
A 70-year old patient with a long-segment Barrett’s esophagus (C5M7) was assessed with an AI algorithm.
Results
The AI system detected a 10 mm focal lesion and AI characterization predicted cancer with a probability of >90%. After ESD resection, histopathology showed mucosal adenocarcinoma (T1a (m), R0) confirming AI diagnosis.
Conclusion
We demonstrate the real-time AI detection, characterization and delineation of a small and early mucosal Barrett’s cancer.
The evaluation and assessment of Barrett’s esophagus is challenging for both expert and nonexpert endoscopists. However, the early diagnosis of cancer in Barrett’s esophagus is crucial for its prognosis, and could save costs. Pre-clinical and clinical studies on the application of Artificial Intelligence (AI) in Barrett’s esophagus have shown promising results. In this review, we focus on the current challenges and future perspectives of implementing AI systems in the management of patients with Barrett’s esophagus.
The growing number of publications on the application of artificial intelligence (AI) in medicine underlines the enormous importance and potential of this emerging field of research.
In gastrointestinal endoscopy, AI has been applied to all segments of the gastrointestinal tract most importantly in the detection and characterization of colorectal polyps. However, AI research has been published also in the stomach and esophagus for both neoplastic and non-neoplastic disorders.
The various technical as well as medical aspects of AI, however, remain confusing especially for non-expert physicians.
This physician-engineer co-authored review explains the basic technical aspects of AI and provides a comprehensive overview of recent publications on AI in gastrointestinal endoscopy. Finally, a basic insight is offered into understanding publications on AI in gastrointestinal endoscopy.
In dieser Arbeit wird die Segmentierung von Gehirngewebe aus Kopfaufnahmen von Ratten mittels Level-Set-Methoden vorgeschlagen. Dazu wird ein zweidimensionaler, kontrastbasierter Ansatz zu einem dreidimensionalen, lokal an die Bildintensität adaptierten Segmentierer erweitert. Es wird gezeigt, dass mit diesem echten 3D-Ansatz die lokalen Bildstrukturen besser berücksichtigt werden können. Insbesondere Magnet-Resonanz-Tomographien (MRTs) mit globalen Helligkeitsgradienten, beispielsweise bedingt durch Oberflächenspulen, können auf diese Weise zuverlässiger und ohne weitere Vorverarbeitungsschritte segmentiert werden. Die Leistungsfähigkeit des Algorithmus wird experimentell an Hand dreier Rattenhirn-MRTs demonstriert.
For accurate registration of histological sections blockface images are frequently used as three dimensional reference. However, due to the use of endocentric lenses the images suffer from perspective errors such as scaling and seemingly relative movement of planes which are located in different distances parallel to the imaging sensor. The suggested correction of those errors is based on the estimation of scaling factors derived from image registration of regions characterized by differing distances to the point of view in neighboring sections. The correction allows the generation of a consistent three dimensional blockface volume.
Virtual training of bone milling requires realtime and realistic haptics of the interaction between the ”virtual mill” and a ”virtual bone”. We propose an exponential abrasion model between a virtual one and the mill bit and combine it with a coarse representation of the virtual bone and the mill shaft for collision detection using the Bullet Physics Engine. We compare our exponential abrasion model to a widely used linear abrasion model and evaluate it quantitatively and qualitatively. The evaluation results show, that we can provide virtual milling in real-time, with an abrasion behavior similar to that proposed in the literature and with a realistic feeling of five different surgeons.
Mathematical analysis of the 1D model and reconstruction schemes for magnetic particle imaging
(2018)
Magnetic particle imaging (MPI) is a promising new in vivo medical imaging modality in which distributions of super-paramagnetic nanoparticles are tracked based on their response in an applied magnetic field. In this paper we provide a mathematical analysis of the modeled MPI operator in the univariate situation. We provide a Hilbert space setup, in which the MPI operator is decomposed into simple building blocks and in which these building blocks are analyzed with respect to their mathematical properties. In turn, we obtain an analysis of the MPI forward operator and, in particular, of its ill-posedness properties. We further get that the singular values of the MPI core operator decrease exponentially. We complement our analytic results by some numerical studies which, in particular, suggest a rapid decay of the singular values of the MPI operator.
Fragestellung: Molekulare Analysen haben deutlich gemacht, dass maligne Tumoren durch Veränderungen des Genoms hervorgerufen werden. Im Gegensatz zu den erblich bedingten Tumorerkrankungen sind die Kenntnisse zur Bedeutung familiärer Faktoren bei den sog. sporadischen Tumoren, insbesondere beim Zervixkarzinom (CX) begrenzt. Material und Methode: Patientinnen des CX-Registers der Universitäts-Frauenklinik wurden retrospektiv bezüglich der Familienanamnese (FA) analysiert. Als positive FA wurde eine maligne Tumorerkrankung bei einem oder mehreren Verwandten ersten Grades definiert. Es wurde unterschieden, ob es sich um Zervixkarzinome, andere gynäkologische Malignome (außer Mammakarzinom) oder eine extragenitale Lokalisation handelte. Diese Daten wurden zum Erkrankungsalter und zum Nachweis pelviner Lymphknotenmetastasen in Beziehung gesetzt. Ergebnisse: 159/737 Frauen (21,6 %) wiesen eine positive FA auf. In 10,7 % der Fälle war mehr als ein Verwandter ersten Grades erkrankt; bei 6,9 % Angehörige ersten und zweiten Grades. Weibliche Verwandte ersten Grades von 42 Frauen wiesen Malignome des weiblichen Genitaltraktes (außer CX) auf. In 6,9 % waren die Mutter und/oder die Schwester an einem Zervixkarzinom erkrankt. Das Durchschnittsalter der Patientinnen mit positiver Familienanamnese (FA) unterschied sich nicht von dem derjenigen mit leerer FA, wohl aber bei getrennter Betrachtung nach der Tumorlokalisation. Bezüglich des Tumorstadiums (pTNM) und der Zahl der Patientinnen mit histologisch nachgewiesenen pelvinen Lymphknotenmetastasen bestand kein Unterschied. Schlussfolgerungen: Die bisherigen Ergebnisse deuten auch beim Zervixkarzinom auf eine familiäre Belastung hin. Dabei existieren offenbar Familien, in denen mit HPV-assoziierte Präkanzerosen bzw. Tumoren vermehrt auftreten. Besonders gefährdet sind Patientinnen, deren weibliche Anverwandte ersten Grades bereits an einer zervikalen Präkanzerose (besonders CIN III) oder einem invasiven CX erkrankt sind. Diese Frauen sollten einem engmaschigen Screening unterzogen werden.
Im Rahmen der Entwicklung eines haptisch-visuellen Trainingssystems für das Fräsen am Felsenbein werden ein Haptikarm und ein autostereoskopischer 3D-Monitor genutzt, um Chirurgen die virtuelle Manipulation von knöchernen Strukturen im Kontext eines sog. Serious Game zu ermöglichen. Unter anderem sollen Assistenzärzte im Rahmen ihrer Ausbildung das Fräsen am Felsenbein für das chirurgische Einsetzen eines Cochlea-Implantats üben können. Die Visualisierung des virtuellen Fräsens muss dafür in Echtzeit und möglichst realistisch modelliert, implementiert und evaluiert werden. Wir verwenden verschiedene Raycasting Methoden mit linearer und Nearest Neighbor Interpolation und vergleichen die visuelle Qualität und die Bildwiederholfrequenzen der Methoden. Alle verglichenen Verfahren sind sind echtzeitfähig, unterscheiden sich aber in ihrer visuellen Qualität.
Die Implantation eines Cochlea Implantates benötigt einen chirurgischen Zugang im Felsenbein und durch die Paukenhöhle des Patienten. Der Chirurg hat eine eingeschränkte Sicht im Operationsgebiet, die weiterhin viele Risikostrukturen enthält. Um eine Cochlea Implantation sicher und fehlerfrei durchzuführen, ist eine umfangreiche theoretische und praktische (teilweise berufsbegleitende) Fortbildung sowie langjährige Erfahrung notwendig. Unter Nutzung von realen klinischen CT/MRT Daten von Innen- und Mittelohr und der interaktiven Segmentierung der darin abgebildeten Strukturen (Nerven, Cochlea, Gehörknöchelchen,...) wird im HaptiVisT Projekt ein haptisch-visuelles Trainingssystem für die Implantation von Innen- und Mittelohr-Implantaten realisiert, das als sog. „Serious Game“ mit immersiver Didaktik gestaltet wird. Die Evaluierung des Demonstrators hinsichtlich Zweckmäßigkeit erfolgt prozessbegleitend und ergebnisorientiert, um mögliche technische oder didaktische Fehler vor Fertigstellung des Systems aufzudecken. Drei zeitlich versetzte Evaluationen fokussieren dabei chirurgisch-fachliche, didaktische sowie haptisch-ergonomische Akzeptanzkriterien.
We present two methods that combine image reconstruction and edge detection in computed tomography (CT) scans. Our first method is as an extension of the prominent filtered backprojection algorithm. In our second method we employ l1-regularization for stable calculation of the gradient. As opposed to the first method, we show that this approach is able to compensate for undersampled CT data.
Der Beitrag untersucht die Nutzung altersgerechter Assistenz-systeme und insbesondere Roboter. Die Studie basiert auf einem Scoping Review, das einen Überblick über aktuelle Forschungsergebnisse zum Einsatz von Robotern in der Pflege gibt, sowie auf Ergebnissen einer qualitativen und quantitativen Studie. In die-ser Untersuchung zeigen sich einige Diffusionshemmnisse, aber auch Chancen für den Einsatz, die anhand eines Fallbeispiels zum Einsatz von Telepräsenzrobotern zu Pflege und Therapie diskutiert werden.
Der Einsatz von Robotern in der Rehabilitation könnte neue Möglichkeiten in der Behandung eröffnen. Maßnahmen im Bereich von Teletherapie und Telenursing, die nach einem Schlaganfall mit einem Telepräsenzroboter durchgeführt werden, erweitern das Spektrum der ambulanten Therapie und Pflege. Wie sehen Betroffene, Angehörige, Pflegende und Therapeut*innen die robotischen Systeme?
Im Fokus der Arbeit stehen Telepräsenzroboter, die im laufenden Projekt TePUS eingesetzt werden. Ziel des Beitrags ist es, die bisherige Nutzung von Telepräsenzrobotern und deren Leistungsspektrum zu erfassen sowie weitere Einsatzbereiche in Pflege, Therapie und Rehabilitation älterer Menschen zu eruieren. Es wurde ein Scoping Review durchgeführt. Als Anwendungsbeispiel werden die im Projekt genutzten bzw. entwickelten Systeme erläutert. Auf Basis dieser Daten werden reelle Einsatzmöglichkeiten von Telepräsenzrobotern in der ambulanten und stationären Altenpflege diskutiert. Nach einer systematischen Sichtung von 310 Artikeln konnten 14 passende Studien gefunden und analysiert werden.
Technik in der Pflege als Generallösung? Neue Perspektiven auf altersgerechte Assistenzsysteme
(2020)
The paper presents a penetration testing framework for automotive IT security education and evaluates its realization. The automotive sector is changing due to automated driving functions, connected vehicles, and electric vehicles. This development also creates new and more critical vulnerabilities. This paper addresses a possible countermeasure, automotive IT security education. Some existing solutions are evaluated and compared with the created Automotive Penetration Testing Education Platform (APTEP) framework. In addition, the APTEP architecture is described. It consists of three layers representing different attack points of a vehicle. The realization of the APTEP is a hardware case and a virtual platform referred to as the Automotive Network Security Case (ANSKo). The hardware case contains emulated control units and different communication protocols. The virtual platform uses Docker containers to provide a similar experience over the internet. Both offer two kinds of challenges.
The first introduces users to a specific interface, while the second combines multiple interfaces, to a complex and realistic challenge. This concept is based on modern didactic theories, such as constructivism and problem-based/challenge-based learning.
Computer Science students from the Ostbayerische Technische Hochschule (OTH) Regensburg experienced the challenges as part of a elective subject. In an online survey evaluated in this paper, they gave positive feedback. Also, a part of the evaluation is the mapping of the ANSKo and the maturity levels in the Software Assurance Maturity Model (SAMM) practice Education & Guidance as well as the SAMM practice Security Testing. The scientific contribution of this paper is to present an APTEP, a corresponding learning concept and an evaluation method.
Sterben im Krankenhaus
(2018)
Die Ausgangssituation des Sterbens wird entlang der bestehenden Sterbeortverhältnisse beschrieben. Ein Sterbeortregister fehlt in Deutschland. Deutlich wird, dass bis heute ca. 80% aller Bundesbürger:innen in stationärem Umfeld versterben. Nach Ansicht verschiedener Expert:innen hat die Covid-19-Pandemie zu einem Verlust erreichter Versorgungsqualitäten geführt. Die weiterhin hervorragende Bedeutung einer gültigen Patienten:innenverfügung für die letzte Lebensphase wird erkennbar.
In einem Projekt wurde die bestehende Situation, dass 20–25% der sterbenden Pflegeheimbewohner:innen nochmals in ein Krankenhaus verlegt werden, zum Ausgangspunkt gewählt, um geeignete Lösungen zu entwickeln. Auch innerhalb der Krankenhäuser kommt es zu Verlegungen Sterbender, obwohl diese sehr häufig weder pflegerisch-medizinisch noch psycho-sozial indiziert sind. Diese beiden Verlegungsprozesse betreffen jährlich ca. 150 000 Patienten:innen bzw. Bewohner:innen. 13 ursächliche, sich zum Teil wechselseitig beeinflussende Faktoren, wurden entlang von Vorarbeiten empirisch und faktorenanalytisch ermittelt. Unter Begleitung des Marburger Instituts der Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften (AWMF) wurden zwei strukturiert-konsensbasierte Leitlinien entwickelt und in einer Pflegeeinrichtung und einem Krankenhaus implementiert. Ergänzend zu diesen Entscheidungshilfen wurden Leitfäden und Managementwerkzeuge entwickelt, welche die Einführung unterstützen.
Als Ergebnis konnte gezeigt werden, dass es nicht nur zu einer Reduktion der Verlegungen, sondern auch zu einer Erhöhung der Mitarbeiter:innenzufriedenheit kam. Die Beteiligten sehen die Möglichkeit, die Leitlinien bundesweit zu transferieren. Das gewählte Projektmanagement, die Anpassungsmöglichkeit der entwickelten Instrumente an regionale Bedingungen, der intersektorale, kollaborative und kommunale Ansatz ermöglichen dies.
Hintergrund:
Knapp 80% aller Menschen sterben in Deutschland in stationärer Versorgung, ca. jeder fünfte von ihnen wird in seiner letzten Lebensphase nochmals in einen anderen Versorgungsbereich verlegt. Dies sind mehr als 150.000 Menschen, die oft ohne Indikation verlegt werden. 13 Risikofaktoren wurden für diese nicht indizierten Verlegungen identifiziert.
Methode:
Unter Begleitung der AWMF wurden zwei regional wirksame Leitlinien entwickelt und in einem Krankenhaus der Maximalversorgung sowie einer Pflegeeinrichtung implementiert. In dem Krankenhaus wurde ein palliativer Konsildienst etabliert. In der Pflegeeinrichtung wurde eine umfassende Personal- und Organisationsentwicklung betrieben. Unterschiedliche Kooperationen mit relevanten regionalen Partnern beider Modelleinrichtungen wurden systematisch ausgeweitet.
Ergebnisse und Schlussfolgerungen:
Die Verlegungen konnten trotz kurzer Laufzeit des Projekts signifikant reduziert werden. Gelingen konnte dies auch durch die Etablierung von Entscheidungshilfen und einer digitalisierten Implementierungsunterstützung. Die Ergebnisse der ethischen und sozialen Begleitforschung rechtfertigen das Vorgehen.Die Zufriedenheit von Angehörigen und Mitarbeitern steigt.
Hat die Coronapandemie samt ihren veränderten gesellschaftlichen Freiheitsbedingungen Einfluss auf die Autonomie von Frauen genommen? Und wie reflektieren Frauen, denen allgemein eine größere Belastung durch z. B. durch gesundheitliche Folgen der Pandemie und sorge- sowie lohnarbeitsbezogenen Aufgaben zugerechnet wird, ihre Autonomiechancen während der Krise? Die Analyse orientiert sich an vier theoretischen Komplexen, die mit dem Pandemieerleben verbunden werden: (1) neue Bedingungen für Freiheit und Autonomie, (2) Belastungen für Frauen, (3) persönliche Neuausrichtung und Selbstbestimmung sowie (4) die Rolle digitaler Technologien und Künstlicher Intelligenz. Der Beitrag, der auf qualitativen Interviewdaten basiert, zeigt, dass die befragten Frauen sich nicht in singulären Lebenspraktiken verorten, sondern während und jenseits pandemischer Zeiten Gemeinschaft als zentralen Wert erachten, der Sinn stiftet. Die Pandemie hat, wenn sie auch Situationen der Fremdbestimmung evoziert hat, für die Frauen daher keinen tiefgreifenden Einfluss auf ihr eigenes Autonomieerleben. Vergeschlechtlichte Prinzipien der Organisation von Sorgearbeit (Care) wurden überraschenderweise kaum problematisiert, auch die Rolle von Digitalität und künstlicher Intelligenz scheint die Autonomiepraxis in der Praxis nur peripher zu berühren; die Suche nach Autonomie ist vielmehr eine andauernde persönliche Aufgabe und Herausforderung im Leben der Frauen.
Vorgestellt wird das Forschungsvorhaben des interdisziplinären Forschungsprojekts „DeinHaus 4.0 – Telepräsenzroboter für die Pflege und Unterstützung von Schlaganfallpatientinnen und -patienten (TePUS)“. Über geeignete Robotersysteme sollen den Proband*innen individuell abgestimmte Angebote aus den Bereichen Telenursing und Teletherapie
zur Verfügung gestellt werden. Hierbei werden einerseits Telepräsenzsitzungen durchgeführt, anderseits kommen verschiedene Apps zum Einsatz. Die Interventionen werden mittels eines Mixed-Methods-Ansatzes evaluiert. Begleitend werden Technikakzeptanz und Nutzungsbereitschaft untersucht.
Training data for Neural Networks is often scarce in the medical domain, which often results in models that struggle to generalize and consequently showpoor performance on unseen datasets. Generally, adding augmentation methods to the training pipeline considerably enhances a model’s performance. Using the dataset of the Foot Ulcer Segmentation Challenge, we analyze two additional augmentation methods in the domain of chronic foot wounds - local warping of wound edges along with projection and blurring of shapes inside wounds. Our experiments show that improvements in the Dice similarity coefficient and Normalized Surface Distance metrics depend on a sensible selection of those augmentation methods.
In this article, we address the challenge of solving the ill-posed reconstruction problem in computed tomography using a translation invariant diagonal frame decomposition (TIDFD). First, we review the concept of a TI-DFD for general linear operators and the corresponding filter-based regularization concept. We then introduce the TI-DFD for the Radon transform on L 2 (R 2) and provide an exemplary construction using the TI wavelet transform. Presented numerical results clearly demonstrate the benefits of our approach over non-translation invariant counterparts.
BACKGROUND
Differentiation of high-flow from low-flow vascular malformations (VMs) is crucial for therapeutic management of this orphan disease.
OBJECTIVE
A convolutional neural network (CNN) was evaluated for differentiation of peripheral vascular malformations (VMs) on T2-weighted short tau inversion recovery (STIR) MRI.
METHODS
527 MRIs (386 low-flow and 141 high-flow VMs) were randomly divided into training, validation and test set for this single-center study. 1) Results of the CNN's diagnostic performance were compared with that of two expert and four junior radiologists. 2) The influence of CNN's prediction on the radiologists' performance and diagnostic certainty was evaluated. 3) Junior radiologists' performance after self-training was compared with that of the CNN.
RESULTS
Compared with the expert radiologists the CNN achieved similar accuracy (92% vs. 97%, p = 0.11), sensitivity (80% vs. 93%, p = 0.16) and specificity (97% vs. 100%, p = 0.50). In comparison to the junior radiologists, the CNN had a higher specificity and accuracy (97% vs. 80%, p < 0.001; 92% vs. 77%, p < 0.001). CNN assistance had no significant influence on their diagnostic performance and certainty. After self-training, the junior radiologists' specificity and accuracy improved and were comparable to that of the CNN.
CONCLUSIONS
Diagnostic performance of the CNN for differentiating high-flow from low-flow VM was comparable to that of expert radiologists. CNN did not significantly improve the simulated daily practice of junior radiologists, self-training was more effective.
Utility of Smartphone-based Three-dimensional Surface Imaging for Digital Facial Anthropometry
(2024)
Background
The utilization of three-dimensional (3D) surface imaging for facial anthropometry is a significant asset for patients undergoing maxillofacial surgery. Notably, there have been recent advancements in smartphone technology that enable 3D surface imaging.
In this study, anthropometric assessments of the face were performed using a smartphone and a sophisticated 3D surface imaging system.
Methods
30 healthy volunteers (15 females and 15 males) were included in the study. An iPhone 14 Pro (Apple Inc., USA) using the application 3D Scanner App (Laan Consulting Corp., USA) and the Vectra M5 (Canfield Scientific, USA) were employed to create 3D surface models. For each participant, 19 anthropometric measurements were conducted on the 3D surface models. Subsequently, the anthropometric measurements generated by the two approaches were compared. The statistical techniques employed included the paired t-test, paired Wilcoxon signed-rank test, Bland–Altman analysis, and calculation of the intraclass correlation coefficient (ICC).
Results
All measurements showed excellent agreement between smartphone-based and Vectra M5-based measurements (ICC between 0.85 and 0.97). Statistical analysis revealed no statistically significant differences in the central tendencies for 17 of the 19 linear measurements. Despite the excellent agreement found, Bland–Altman analysis revealed that the 95% limits of agreement between the two methods exceeded ±3 mm for the majority of measurements.
Conclusion
Digital facial anthropometry using smartphones can serve as a valuable supplementary tool for surgeons, enhancing their communication with patients. However, the proposed data suggest that digital facial anthropometry using smartphones may not yet be suitable for certain diagnostic purposes that require high accuracy.
Purpose: In this trial, we used a previously developed prototype software to assess aesthetic results after reconstructive surgery for congenital breast asymmetry using automated anthropometry. To prove the consensus between the manual and automatic digital measurements, we evaluated the software by comparing the manual and automatic measurements of 46 breasts.
Methods: Twenty-three patients who underwent reconstructive surgery for congenital breast asymmetry at our institution were examined and underwent 3D surface imaging. Per patient, 14 manual and 14 computer-based anthropometric measurements were obtained according to a standardized protocol. Manual and automatic measurements, as well as the previously proposed Symmetry Index (SI), were compared.
Results: The Wilcoxon signed-rank test revealed no significant differences in six of the seven measurements between the automatic and manual assessments. The SI showed robust agreement between the automatic and manual methods.
Conclusion: The present trial validates our method for digital anthropometry. Despite the discrepancy in one measurement, all remaining measurements, including the SI, showed high agreement between the manual and automatic methods. The proposed data bring us one step closer to the long-term goal of establishing robust instruments to evaluate the results of breast surgery.
Background
Breast reconstruction is an important coping tool for patients undergoing a mastectomy. There are numerous surgical techniques in breast reconstruction surgery (BRS). Regardless of the technique used, creating a symmetric outcome is crucial for patients and plastic surgeons. Three-dimensional surface imaging enables surgeons and patients to assess the outcome’s symmetry in BRS. To discriminate between autologous and alloplastic techniques, we analyzed both techniques using objective optical computerized symmetry analysis. Software was developed that enables clinicians to assess optical breast symmetry using three-dimensional surface imaging.
Methods
Twenty-seven patients who had undergone autologous (n = 12) or alloplastic (n = 15) BRS received three-dimensional surface imaging. Anthropomorphic data were collected digitally using semiautomatic measurements and automatic measurements. Automatic measurements were taken using the newly developed software. To quantify symmetry, a Symmetry Index is proposed.
Results
Statistical analysis revealed that there is no dif- ference in the outcome symmetry between the two groups (t test for independent samples; p = 0.48, two-tailed).
Conclusion
This study’s findings provide a foundation for qualitative symmetry assessment in BRS using automatized digital anthropometry. In the present trial, no difference in the outcomes’ optical symmetry was detected between autologous and alloplastic approaches.
Hintergrund: Die Entwicklung assistiver Technologien wird in den kommenden Jahren nicht nur in der Chirurgie von zunehmender Bedeutung sein. Die Wahrnehmung der Istsituation stellt hierbei die Grundlage jeder autonomen Handlung dar. Hierfür können unterschiedliche Sensorsysteme genutzt werden, wobei videobasierte Systeme ein besonderes Potenzial aufweisen.
Methode: Anhand von Literaturangaben und auf Basis eigener Forschungsarbeiten werden zentrale Aspekte bildbasierter Unterstützungssysteme für die Chirurgie dargestellt. Hierbei wird deren Potenzial, aber auch die Limitationen der Methoden erläutert.
Ergebnisse: Eine etablierte Anwendung stellt die Phasendetektion chirurgischer Eingriffe dar, für die Operationsvideos mittels neuronaler Netzwerke analysiert werden. Durch eine zeitlich gestützte und transformative Analyse konnten die Ergebnisse der Prädiktion jüngst deutlich verbessert werden. Aber auch robotische Kameraführungssysteme nutzen Bilddaten, um das Laparoskop zukünftig autonom zu navigieren. Um die Zuverlässigkeit an die hohen Anforderungen in der Chirurgie anzugleichen, müssen diese jedoch durch zusätzliche Informationen ergänzt werden. Ein vergleichbarer multimodaler Ansatz wurde bereits für die Navigation und Lokalisation bei laparoskopischen Eingriffen umgesetzt. Hierzu werden Videodaten mittels verschiedener Methoden analysiert und diese Ergebnisse mit anderen Sensormodalitäten fusioniert.
Diskussion: Bildbasierte Unterstützungsmethoden sind bereits für diverse Aufgaben verfügbar und stellen einen wichtigen Aspekt für die Chirurgie der Zukunft dar. Um hier jedoch zuverlässig und für autonome Funktionen eingesetzt werden zu können, müssen sie zukünftig in multimodale Ansätze eingebettet werden, um die erforderliche Sicherheit bieten zu können.
Video Documentation of endoscopic findings simplifies diagnostic counseling of the patient and aids pre-operative discussion among the medical team. Judgment of such images is still subjective and can not give a quantitative evaluation of the disease process regarding diagnosis or response to treatment. Modern treatment of early laryngeal cancer with laserablation requires intensive follow up and frequent direct laryngoscopy under general anesthesia with blind biopsies to detect any tumor residual or recurrence. Inflammatory conditions of the larynx are frequently confused with other causes of dysphonia. Mapping anddigital analysis of the documented image will suggest the tumor site and avoids undue blind biopsies under anesthesia. However, varying illumination results in different colors reflected from the same object. To achieve quantitative analysis, color constancy has to be assured. Inthis paper, the environment is presented which allow the objective judgment of larngoscopies.
Das Forschungsprojekt „Dein Haus 4.0 Oberpfalz – Telepräsenzroboter für die Pflege und Unterstützung von Schlaganfallpatientinnen und -patienten (TePUS)“ untersucht den Einsatz von zwei Varianten von Telepräsenzrobotern. Das Arbeitspapier stellt die sozialwissenschaftliche Begleitforschung des Projekts vor. Diese hat zum einen zum Ziel, Akzeptanz und Potenzial der eingesetzten technischen Assistenzsysteme empirisch zu untersuchen. Daneben werden ethisch, rechtlich, gesellschaftlich und organisatorisch relevante Fragestellungen des Technikeinsatzes und der Mensch-Technik-Interaktion analysiert (ELSI-Begleitstudie).
Seit dem Beginn des russischen Angriffskriegs und der Ukraine-Krise im Februar 2022 wurden massive Preissteigerungen auf dem Energiesektor beobachtet. Die Unsicherheiten auf dem Energiemarkt sowie die angespannte Lage der Gasversorgung führten zu erheblich höheren Strompreisen für Privathaushalte. Welche Faktoren den Stromverbrauch determinieren, wird im vorliegenden Arbeitspapier genauer untersucht. Der Zusammenhang zwischen Haushaltsmerkmalen und deren Stromverbrauch ist in der sozialwissenschaftlichen Energieforschung bekannt. Eine Möglichkeit der Verbrauchserfassung von Strom sind Smart-Meter, digitale Stromzähler.
Projektziel von EVEKT ist die Untersuchung von Möglichkeiten zur Reduktion des Stromverbrauchs von privaten Haushalten. Ziel dieses Arbeitspapiers ist es, verschiedene Gruppen von Haushalten und deren Charakteristik bei Strombezug und Smart-Meter-Nutzung zu differenzieren. Damit dokumentiert es einen Arbeitsschritt im Rahmen eines EVEKT-Teilprojekts zur Untersuchung sozialer und ethischer Aspekte von KI-Technologie in Privathaushalten.
Es wurde ein Scoping Review zu Stromverbrauchsindikatoren in Bezug auf Smart-Meter durchgeführt. In einer systematischen Suche in der Datenbank Web of Science wurden 477 Treffer gefunden. Von den 477 Treffern konnten nach Durchsicht 12 passende Studien analysiert werden.
Die untersuchten Arbeiten unterteilen Haushalte in verschiedene Kategorien bzw. Cluster. Einige Studien gruppieren nach soziodemografischen Aspekten, während andere die Verbrauchsmuster analysieren. Als Ergebnis des Scoping Reviews empfehlen wir, beide Ansätze zu kombinieren, um verschiedene Haushaltstypen angemessen anzusprechen.
Bedeutung für die weiteren Projektschritte (Bevölkerungsumfrage)
Inwieweit die Zugehörigkeit zu bestimmten Haushaltstypen die Akzeptanz von KI-Technologie und feedback-basierter Apps determinieren, wird im Rahmen einer eigenen Befragung überprüft werden. Dazu wird aktuell ein Fragebogen entwickelt. Die Feldphase der deutschlandweiten Bevölkerungsbefragung ist für 2024 geplant.
Telepräsenzroboter für die Pflege und Unterstützung von Schlaganfallpatinnen und -patienten (TePUS)
(2020)
Der Beitrag untersucht Einflussfaktoren der Impfbereitschaft mit einem COVID-19-Vakzin in Deutschland. Die Querschnittstudie basiert auf einer bundesweiten telefonischen Bevölkerungsbefragung bei einer Zufallsauswahl der Bevölkerung (November bis Dezember 2020). Determinanten der Impfbereitschaft werden bivariat und mit einem multivariaten logistischen Regressionsmodell analysiert. Neben dem Geschlecht, dem Schulabschluss, der Elternschaft von minderjährigen Kindern und der Zugehörigkeit zu einer Risikogruppe hängt die Impfbereitschaft mit dem Wissenschaftsvertrauen, dem kollektiven Verantwortungsgefühl sowie mit Impfverschwörungs- und allgemeinen Verschwörungsüberzeugungen zusammen. Das Alter ist ein Prädiktor mit geringem Einfluss. Nicht bestätigen lässt sich die Annahme, dass Vertrauen in das Robert Koch-Institut oder Vertrauen in klassische Medien einen Einfluss haben.