Lebenswissenschaften und Ethik
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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.
Degradation and release of tannic acid from an injectable tissue regeneration bead matrix in vivo
(2021)
The development of multifunctional biomaterials as both tissue regeneration and drug delivery devices is currently a major focus in biomedical research. Tannic Acid (TA), a naturally occurring plant polyphenol, displays unique medicinal abilities as an antioxidant, an antibiotic, and as an anticancer agent. TA has applications in biomaterials acting as a crosslinker in polymer hydrogels improving thermal stability and mechanical properties. We have developed injectable cell seeded collagen beads crosslinked with TA for breast reconstruction and anticancer activity following lumpectomy. This study determined the longevity of the bead implants by establishing a degradation time line and TA release profile in vivo. Beads crosslinked with 0.1% TA and 1% TA were compared to observe the differences in TA concentration on degradation and release. We found collagen/TA beads degrade at similar rates in vivo, yet are resistant to complete degradation after 16 weeks. TA is released over time in vivo through diffusion and cellular activity. Changes in mechanical properties in collagen/TA beads before implantation to after 8 weeks in vivo also indicate loss of TA over a longer period of time. Elastic moduli decreased uniformly in both 0.1% and 1% TA beads. This study establishes that collagen/TA materials can act as a drug delivery system, rapidly releasing TA within the first week following implantation. However, the beads retain TA long term allowing them to resist degradation and remain in situ acting as a cell scaffold and tissue filler. This confirms its potential use as an anticancer and minimally invasive breast reconstructive device following lumpectomy.
CRISPR (clustered regularly interspaced short palindromic repeats) systems are one of the most fascinating tools of the current era in molecular biotechnology. With the ease that they provide in genome editing, CRISPR systems generate broad opportunities for targeting mutations. Specifically in recent years, disease-causing mutations targeted by the CRISPR systems have been of main research interest; particularly for those diseases where there is no current cure, including cancer. KRAS mutations remain untargetable in cancer. Mutations in this oncogene are main drivers in common cancers, including lung, colorectal and pancreatic cancers, which are severe causes of public health burden and mortality worldwide, with no cure at hand. CRISPR systems provide an opportunity for targeting cancer causing mutations. In this review, we highlight the work published on CRISPR applications targeting KRAS mutations directly, as well as CRISPR applications targeting mutations in KRAS-related molecules. In specific, we focus on lung, colorectal and pancreatic cancers. To date, the limited literature on CRISPR applications targeting KRAS, reflect promising results. Namely, direct targeting of mutant KRAS variants using various CRISPR systems resulted in significant decrease in cell viability and proliferation in vitro, as well as tumor growth inhibition in vivo. In addition, the effect of mutant KRAS knockdown, via CRISPR, has been observed to exert regulatory effects on the downstream molecules including PI3K, ERK, Akt, Stat3, and c-myc. Molecules in the KRAS pathway have been subjected to CRISPR applications more often than KRAS itself. The aim of using CRISPR systems in these studies was mainly to analyze the therapeutic potential of possible downstream and upstream effectors of KRAS, as well as to discover further potential molecules. Although there have been molecules identified to have such potential in treatment of KRAS-driven cancers, a substantial amount of effort is still needed to establish treatment strategies based on these discoveries. We conclude that, at this point in time, despite being such a powerful directed genome editing tool, CRISPR remains to be underutilized for targeting KRAS mutations in cancer. Efforts channelled in this direction, might pave the way in solving the long-standing challenge of targeting the KRAS mutations in cancers.
Blood flow in channels of varying diameters <500μm exhibits strong non-linear effects. Multiphase finite volume approaches are feasible, but still computationally costly. Here, the feasibility of applying convolutional neural networks for blood flow prediction in artificial lungs is investigated. Training targets are precomputed using an Eulerian two-phase approach. To match with experimental data, the interphase drag and lift, as well as intraphase shear-thinning are adapted. A recursively branching regression network and convolution/deconvolution networks with plain skip connections and densely connected skips are investigated. A priori knowledge is incorporated in the loss functional to prevent the network from learning non-physical solutions. Inference from neural networks is approximately six orders of magnitude faster than the classical finite volume approach. Even if resulting in comparably coarse flow fields, the neural network predictions can be used as close to convergence initial solutions greatly accelerating classical flow computations.
Introduction Paediatricians, general practitioners (GPs) and midwives in primary care are important sources of information for parents on early childhood allergy prevention (ECAP). Research has shown that preventive counselling by health professionals can be effective in improving patients' health literacy (HL) and health behaviour. Providing effective advice relies on two factors. First, health professionals need be up-to-date with research evidence on ECAP, to consider popular misconceptions and fears and to translate this knowledge into clear recommendations for parents (knowledge translation). Second, they need to know and apply counselling techniques and create a practice setting which accommodates parental HL needs (health literacy-responsive care). The objective of this study is to explore and assess how German health professionals take up and translate ECAP evidence into appropriate recommendations for parents, how they consider HL in counselling and practice organisation and what barriers and enablers they find in their performance of HL-responsive ECAP. Methods and analysis The study has a sequential mixed-method design, in two phases. In the first phase, qualitative semi-structured expert interviews will be conducted with health professionals (paediatricians, GPs and midwives) at primary care level and professional policy level. Data collection is ongoing until January 2022. In the second phase, based on the qualitative results, a standardised questionnaire will be developed, and pilot-tested in a wider population of German health professionals. The findings of both phases will be integrated. Ethics and dissemination The study has received ethical approval from the Ethics Committee of the University of Regensburg (18-1205-101). The results will be published in international peer-reviewed open access journals and via presentations at scientific conferences. The results will also be shared with German health professionals, decision-makers and potential funders of interventions.
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.
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.
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.
Due to the COVID-19 pandemic, the course Communication Systems was transferred from presence to a virtual lecture. No hands-on lab experiments were possible. Therefore, a GNU Radio practical part was integrated into the lecture. A tutorial was written to guide students through the first steps with GNU Radio to decrease initial problems and increase student motivation. Several examples were given regarding digital signal processing, analogue and digital modulation and demodulation. Software Defined Radio receivers were set up in the amateur radio lab of the university and connected to the internet so that students were able to get real radio data to be analyzed and demodulated with their own GNU Radio models. This enables a knowledge transfer from simulated signal processing to the analysis and demodulation of real live signals. Students showed medium to high interest in practical examples during the lectures and tried their own GNU Radio simulations. Overall, the integration of GNU Radio and Software Defined Radio receivers into this virtual lecture gave additional benefits for the students to deal with signal processing and demodulation experiments. It is planned to use GNU Radio in the future for complementary practical examples to the hands-on experiments in the lab.
One of the fundamental challenges in L-PBF of filigree geometries, such as aortic stents used in biomedical applications, is the requirement for a robust yet easily removable support structure that allows each component to be successfully fabricated without distortion. To solve this challenge, an integrative experimental approach was attempted in the present study by identifying an optimal support structure design and an optimized support removal strategy for this design. The specimens were manufactured using four different support structure designs based on the geometry exposed to the laser beam during the L-PBF. Support removal procedures included sand blasting (SB), glass bead blasting (GB), and electrochemical polishing (ECP). The two best-performing designs (line and cross) were chosen due to shorter lead times and lower material consumption. As an additional factor that indicates a stable design, the breaking load requirement to remove the support structures was determined. A modified line support with a 145° included angle was shown to be the best support structure design in terms of breaking load, material consumption, and manufacturing time. All three procedures were used to ensure residue-free support removal for this modified line support design, with ECP proving to be the most effective.
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.
In recent years, our society faced a massive interconnection of computer-based everyday objects, which opens these items for cyber-attacks. Dependent on the physical capabilities, successful attacks can vary from data exposure or a loss of functionality to a threat to life and limb. Connected- and autonomous vehicles are extremely safety-critical systems with a huge damage potential. This global trend, together with existing and upcoming regulations (ISO 270xx, ISO 21434, UNECE WP.29, UNECE R155), and the lack of qualified professionals create a tension field for the entire automotive industry.
Hence, new education concepts for engineers of safety-critical and connected systems are necessary to secure our daily and future systems against cyber-attacks and raise awareness and knowledge of the topic of IT-Security. Existing automotive security education systems have one common problem: All systems are hardware-based and therefore have very steep learning curves for beginners. Hardware-based systems, in general, are expensive in their initial costs, require regular maintenance, and add diverse operational difficulties independent of the aspired education goal. Additionally, the global pandemic increased the necessity of virtual education concepts for security training in cyber-physical systems.
Therefore, we present a novel concept for the education of cyber-security professionals for automotive systems based on discovery and problem-based learning in a virtual learning environment (VLE). Our concept contains individual exercises focusing on the topics of vulnerabilities and attacks in automotive networks and systems. Each exercise relates those topics to the corresponding security goals and countermeasures for mitigation. The learners work collaboratively in a self-contained manner within the VLE to acquire the necessary information to answer questions or find a solution to the given problem. To consider the heterogeneous background (e.g. knowledge, experience, preconceptions) of the learners, the topics can be presented in different difficulties, enabling an adaptable learning environment and different learning trajectories within the exercises.
The concept is based on a VLE, consisting of automotive networks and components, which simulate the behavior of a vehicle. This environment provides a hands-on, "real-life" scenario, which allows discovery and problem-based learning in a realistic, but cheap and scalable education environment. Furthermore, virtualization removes common difficulties, always present in training on real hardware. This aims to decrease complexity, prevent learning obstacles related to hardware handling, and enables a location-independent learning environment.
The target group of our education concept is Bachelor and Master students of computer science, engineering (e.g. electrical engineering, mechatronics), or similar studies, and (experienced) engineers from the industry.
In summary, our publication contains two contributions. We present an adaptable virtual learning environment for automotive security education, combined with an educational concept based on discovery and problem-based learning techniques. The goals of our concept are the education of cyber-security professionals for safety-critical, connected automotive systems and the support of life-long learning reaching from academic education to training in the industry.
Results of WICOVIR Gargle Pool PCR Testing in German Schools Based on the First 100,000 Tests
(2021)
Background:
Opening schools and keeping children safe from SARS-CoV-2 infections at the same time is urgently needed to protect children from direct and indirect consequences of the COVID-19 pandemic. To achieve this goal, a safe, efficient, and cost-effective SARS-CoV-2 testing system for schools in addition to standard hygiene measures is necessary.
Methods:
We implemented the screening WICOVIR concept for schools in the southeast of Germany, which is based on gargling at home, pooling of samples in schools, and assessment of SARS-CoV-2 by pool rRT-PCR, performed decentralized in numerous participating laboratories. Depooling was performed if pools were positive, and results were transmitted with software specifically developed for the project within a day. Here, we report the results after the first 13 weeks in the project.
Findings:
We developed and implemented the proof-of-concept test system within a pilot phase of 7 weeks based on almost 17,000 participants. After 6 weeks in the main phase of the project, we performed >100,000 tests in total, analyzed in 7,896 pools, identifying 19 cases in >100 participating schools. On average, positive children showed an individual CT value of 31 when identified in the pools. Up to 30 samples were pooled (mean 13) in general, based on school classes and attached school staff. All three participating laboratories detected positive samples reliably with their previously established rRT-PCR standard protocols. When self-administered antigen tests were performed concomitantly in positive cases, only one of these eight tests was positive, and when antigen tests performed after positive pool rRT-PCR results were already known were included, 3 out of 11 truly positive tests were also identified by antigen testing. After 3 weeks of repetitive WICOVIR testing twice weekly, the detection rate of positive children in that cohort decreased significantly from 0.042 to 0.012 (p = 0.008).
Interpretation:
Repeated gargle pool rRT-PCR testing can be implemented quickly in schools. It is an effective, valid, and well-received test system for schools, superior to antigen tests in sensitivity, acceptance, and costs.
Academic self-regulation is a key factor for motivation and learning achievement. Yet with the large range of individual factors, this is not a one-size-fits-all proposition. This study of L2 Technical English students at two German universities explored learners’ expectations and motivations, in particular regarding self-regulation and self-efficacy via the individual’s time investment in self-led study. In an initial survey, learners (N=1646) reported on their English skill levels and anticipated learning habits. Complementarily, the retrospective survey investigated learners’ (N=796) actual behavior during the course, their perceptions of language skill improvement, and their satisfaction. The initial survey indicates a clear understanding that time investment in self-regulated study will lead to greater improvement, an outcome confirmed in the retrospective survey. Additionally, students who invested more time in their coursework were more satisfied with their achievement, although most learners acknowledge they should have studied more. The results verify that learners recognize the nexus between self-regulation and language skill improvement, yet university students are not satisfied with their capacity to self-regulate their language learning strategies. While differences in students’ skill levels and academic self-efficacy result in divergent degrees of progress, students of all types report benefits to their language skills when motivated to self-regulated study.
Academic self-regulation is a key factor for motivation and learning achievement. Yet with the large range of individual factors, this is not a one-size-fits-all proposition. This study of L2 Technical English students at two German universities explored learners’ expectations and motivations, in particular regarding self-regulation and self-efficacy via the individual’s time investment in self-led study. In an initial survey, learners (N=1646) reported on their English skill levels and anticipated learning habits. Complementarily, the retrospective survey investigated learners’ (N=796) actual behavior during the course, their perceptions of language skill improvement, and their satisfaction. The initial survey indicates a clear understanding that time investment in self-regulated study will lead to greater improvement, an outcome confirmed in the retrospective survey. Additionally, students who invested more time in their coursework were more satisfied with their achievement, although most learners acknowledge they should have studied more. The results verify that learners recognize the nexus between self-regulation and language skill improvement, yet university students are not satisfied with their capacity to self-regulate their language learning strategies. While differences in students’ skill levels and academic self-efficacy result in divergent degrees of progress, students of all types report benefits to their language skills when motivated to self-regulated study.
"Topographic Shift": a new digital approach to evaluating topographic changes of the female breast
(2021)
Purpose
To assess precise topographic changes of the breast, objective documentation and evaluation of pre- and postoperative results are crucial. New technologies for mapping the body using digital, three-dimensional surface measurements have offered novel ways to numerically assess the female breast. Due to the lack of clear demarcation points of the breast contour, the selection of landmarks on the breast is highly dependent on the examiner, and, therefore, is prone to error when conducting before-after comparisons of the same breast. This study describes an alternative to volumetric measurements, focusing on topographic changes of the female breast, based on three-dimensional scans.
Method
The study was designed as an interventional prospective study of 10 female volunteers who had planned on having aesthetic breast augmentation with anatomical, textured implants. Three dimensional scans of the breasts were performed intraoperatively, first without and then with breast implants. The topographic change was determined as the mean distance between two three-dimensional layers before and after augmentation. This mean distance is defined as the Topographic Shift. Results The mean implant volume was 283 cc (SD = 68.6 cc, range = 210-395 cc). The mean Topographic Shift was 7.4 mm (SD = 1.9 mm, range = 4.8-10.7 mm). The mean Topographic Shifts per quadrant were: I: 8.0 mm (SD = 3.3 mm); II: 9.2 mm (SD = 3.1 mm); III: 6.9 mm (SD = 3.5 mm); IV: 1.9 mm (SD = 4.3 mm).
Conclusion
The Topographic Shift, describing the mean distance between two three-dimensional layers (for example before and after a volume changing therapy), is a new approach that can be used for assessing topographic changes of a body area. It was found that anatomical, textured breast implants cause a topographic change, particularly on the upper breast, in quadrant II, the decollete.
Regarding the prevention of injuries and rehabilitation of the human hand, musculoskeletal simulations using an inverse dynamics approach allow for insights of the muscle recruitment and thus acting forces on the hand. Currently, several hand models from various research groups are in use, which are mainly validated by the comparison of numerical and anatomical moment arms. In contrast to this validation and model-building technique by cadaver studies, the aim of this study is to further validate a recently published hand model [1] by analyzing numerically calculated muscle activities in comparison to experimentally measured electromyographical signals of the muscles. Therefore, the electromyographical signals of 10 hand muscles of five test subjects performing seven different hand movements were measured. The kinematics of these tasks were used as input for the hand model, and the numerical muscle activities were computed. To analyze the relationship between simulated and measured activities, the time difference of the muscle on- and off-set points was calculated, which resulted in a mean on- and off-set time difference of 0.58 s between the experimental data and the model. The largest differences were detected for movements that mainly addressed the wrist. One major issue comparing simulated and measured muscle activities of the hand is cross-talk. Nevertheless, the results show that the hand model fits the experiment quite accurately despite some limitations and is a further step toward patient-specific modeling of the upper extremity.
Musculoskeletal lower back load of accoucheurs during childbirth – A pilot and feasibility study
(2021)
Introduction: Back problems represent one of the leading causes of accouchers' work-related musculoskeletal morbidities. The correct execution of birth-related maneuvers including manual perineal protection is crucial not only for the mother and child but also for obstetricians and midwives to reduce any strain on their musculoskeletal system. Therefore, the overall aim of this study was to test the feasibility of determining the effect of different accouchers' postures (standing and kneeling) on their musculoskeletal system.
Methods: The biomechanical analysis is based on musculoskeletal simulations that included motion recordings of real deliveries as well as deliveries conducted on a birthing simulator. These simulations were then used to determine individual joints' loads.
Results: In the kneeling posture, both a low intra-operator variability and a lower average maximum load of the lower back was observed. For the standing position the spine load was reduced by pivoting the elbow on the accouchers' thigh, which in turn was associated with a significantly greater load on the shoulder joint.
Conclusion: The study demonstrated the feasibility of our technique to assess joints loads. It also provided initial data indicating that a posture that reduces spinal flexion and tilt, achieved in this study by the kneeling, can significantly reduce the strain on the practitioner's musculoskeletal system.
Outputs of musculoskeletal models should be considered probabilistic rather than deterministic as they are affected by inaccuracies and estimations associated with the development of the model. One of these uncertainties being critical for modeling arises from the determination of the muscles' line of action and the physiological cross-sectional area. Therefore, the aim of this study was to evaluate the outcome sensitivity of model predictions from a musculoskeletal hand model in comparison to the uncertainty of these input parameters. For this purpose, the kinematics and muscle activities of different hand movements (abduction of the fingers, abduction of the thumb, and flexion of the thumb) were recorded. One thousand simulations were calculated for each movement using the Latin hypercube sampling method with a corresponding variation of the muscle origin/insertion points and the cross-sectional area. Comparing the standard hand to simulations incorporating uncertainties of input parameters shows no major deviations in on- and off-set time point of muscle activities. About 60% of simulations are located within a ± 30% interval around the standard model concerning joint reaction forces. The comparison with the variation of the input data leads to the conclusion that the standard hand model is able to provide not over-scattered outcomes and, therefore, can be considered relatively stable. These results are of practical importance to the personalization of a musculoskeletal model with subject-specific bone geometries and hence changed muscle line of action.
Purpose
Congenital breast asymmetry is a serious gynecological malformation for affected patients. The condition hits young women in puberty and is associated with socio-esthetic handicap, depression, and psychosexual problems. Surgical treatment is usually early in the patient's lifetime, so a long-term sustainable solution is important. Although postoperative outcome has been evaluated in several studies before, this study is the first to analyze which objective parameters have the greatest influence on subjective satisfaction with long-term results.
Methods
Thirty-four patients diagnosed with congenital breast asymmetry that underwent either lipofilling or implant therapy between the years of 2008 to 2019 were examined. On average, our collective comprised patients seven years after surgery. Data were mainly gathered through manual measurements, patient-reported outcome measures (Breast Q™), and breast volumetry based on 3D scans (Vectra® H2, Canfield Scientific).
Results
Among all analyzed parameters, only areolar diameter correlated significantly negatively with the subjective outcome satisfaction of the patient. Regarding the subjective assessment of postoperative satisfaction with similarity of the breasts, again the mean areolar diameter, but also the difference in areolar diameter and breast volume between the right and left breasts correlated significantly negatively.
Conclusion
Areolar diameter was revealed as being a significant factor influencing subjective long-term satisfaction in breast asymmetry patients. Moreover, 3D volumetry proves to be an effective tool to substantiate subjective patient assessments. Our findings may lead to further improvements to surgical planning and will be expanded in further studies.
Brain lesions in language-related cortical areas remain a challenge in the clinical routine. In recent years, the resting-state fMRI (RS-fMRI) was shown to be a feasible method for preoperative language assessment. The aim of this study was to examine whether language-related resting-state components, which have been obtained using a data-driven independent-component-based identification algorithm, can be supportive in determining language dominance in the left or right hemisphere. Twenty patients suffering from brain lesions close to supposed language-relevant cortical areas were included. RS-fMRI and task-based (TB-fMRI) were performed for the purpose of preoperative language assessment. TB-fMRI included a verb generation task with an appropriate control condition (a syllable switching task) to decompose language-critical and language-supportive processes. Subsequently, the best fitting ICA component for the resting-state language network (RSLN) referential to general linear models (GLMs) of the TB-fMRI (including models with and without linguistic control conditions) was identified using an algorithm based on the Dice index. Thereby, the RSLNs associated with GLMs using a linguistic control condition led to significantly higher laterality indices than GLM baseline contrasts. LIs derived from GLM contrasts with and without control conditions alone did not differ significantly. In general, the results suggest that determining language dominance in the human brain is feasible both with TB-fMRI and RS-fMRI, and in particular, the combination of both approaches yields a higher specificity in preoperative language assessment. Moreover, we can conclude that the choice of the language mapping paradigm is crucial for the mentioned benefits.
Aims
Eosinophilic esophagitis (EoE) is easily missed during endoscopy, either because physicians are not familiar with its endoscopic features or the morphologic changes are too subtle. In this preliminary paper, we present the first attempt to detect EoE in endoscopic white light (WL) images using a deep learning network (EoE-AI).
Methods
401 WL images of eosinophilic esophagitis and 871 WL images of normal esophageal mucosa were evaluated. All images were assessed for the Endoscopic Reference score (EREFS) (edema, rings, exudates, furrows, strictures). Images with strictures were excluded. EoE was defined as the presence of at least 15 eosinophils per high power field on biopsy. A convolutional neural network based on the ResNet architecture with several five-fold cross-validation runs was used. Adding auxiliary EREFS-classification branches to the neural network allowed the inclusion of the scores as optimization criteria during training. EoE-AI was evaluated for sensitivity, specificity, and F1-score. In addition, two human endoscopists evaluated the images.
Results
EoE-AI showed a mean sensitivity, specificity, and F1 of 0.759, 0.976, and 0.834 respectively, averaged over the five distinct cross-validation runs. With the EREFS-augmented architecture, a mean sensitivity, specificity, and F1-score of 0.848, 0.945, and 0.861 could be demonstrated respectively. In comparison, the two human endoscopists had an average sensitivity, specificity, and F1-score of 0.718, 0.958, and 0.793.
Conclusions
To the best of our knowledge, this is the first application of deep learning to endoscopic images of EoE which were also assessed after augmentation with the EREFS-score. The next step is the evaluation of EoE-AI using an external dataset. We then plan to assess the EoE-AI tool on endoscopic videos, and also in real-time. This preliminary work is encouraging regarding the ability for AI to enhance physician detection of EoE, and potentially to do a true “optical biopsy” but more work is needed.
Biomechanical Analysis of the Right Elevated Glenohumeral Joint in Violinists during Legato-Playing
(2021)
BACKGROUND:
Many statistics reveal that violin players suffer most often from musculoskeletal disorders compared to musicians of other instrument groups. A common phenomenon, especially observed in violin beginners, is the tendency to elevate the right shoulder during playing the violin. This can probably lead to serious disorders in long-term practice with repetitive movements.
OBJECTIVE:
For this reason, this study investigated the relationship between the right shoulder elevation and the force in the right glenohumeral joint during violin playing. It was hypothesized that the forces in the right glenohumeral joint are higher during playing with the right shoulder raised compared to playing in normal posture.
METHODS:
Motion capture data from four experienced violinists was recorded and processed by means of musculoskeletal simulation to get the force and elevation angle while playing with raised shoulder and in normal position.
RESULTS:
The results indicate that the absolute values of the resulting force, as well as the forces in the mediolateral, inferosuperior, and anteroposterior directions, are higher in playing the violin with the shoulder raised than in a normal posture.
CONCLUSIONS: Elevating the right shoulder while playing the violin may pose a potential problem.
Objectives
The European University Association (EUA) defines “employability” as a major goal of higher education. Therefore, competence-based orientation is an important aspect of education. The representation of a standardized job profile in the field of medical informatics, which is based on the most common labor market requirements, is fundamental for identifying and conveying the learning goals corresponding to these competences.
Methods
To identify the most common requirements, we extracted 544 job advertisements from the German job portal, STEPSTONE. This process was conducted via a program we developed in R with the “rvest” library, utilizing web crawling, web extraction, and text mining. After removing duplicates and filtering for jobs that required a bachelor's degree, 147 job advertisements remained, from which we extracted qualification terms. We categorized the terms into six groups: professional expertise, soft skills, teamwork, processes, learning, and problem-solving abilities.
Results
The results showed that only 45% of the terms are related to professional expertise, while 55% are related to soft skills. Studies of employee soft skills have shown similar results. The most prevalent terms were programming, experience, project, and server. Our second major finding is the importance of experience, further underlining how essential practical skills are.
Conclusions
Previous studies used surveys and narrative descriptions. This is the first study to use web crawling, web extraction, and text mining. Our research shows that soft skills and specialist knowledge carry equal weight. The insights gained from this study may be of assistance in developing curricula for medical informatics.
Embodiment theories have proposed a reciprocal relationship between emotional state and bodily reactions. Besides large body postures, recent studies have found emotions to affect rather subtle bodily expressions, such as slumped or upright sitting posture. This study investigated back muscle activity as an indication of an effect of positive and negative emotions on the sitting position. The electromyography (EMG) activity of six back muscles was recorded in 31 healthy subjects during exposure to positive and negative affective pictures. A resting period was used as a control condition. Increased muscle activity patterns in the back were found during the exposure to negative emotional stimuli, which was mainly measured in the lumbar and thorax regions. The positive emotion condition caused no elevated activity. The findings show that negative emotions lead to increased differential muscle activity in the back and thus corroborate those of previous research that emotion affects subtle bodily expressions.
Aims
Celiac disease (CD) is a complex condition caused by an autoimmune reaction to ingested gluten. Due to its polymorphic manifestation and subtle endoscopic presentation, the diagnosis is difficult and thus the disorder is underreported. We aimed to use deep learning to identify celiac disease on endoscopic images of the small bowel.
Methods
Patients with small intestinal histology compatible with CD (MARSH classification I-III) were extracted retrospectively from the database of Augsburg University hospital. They were compared to patients with no clinical signs of CD and histologically normal small intestinal mucosa. In a first step MARSH III and normal small intestinal mucosa were differentiated with the help of a deep learning algorithm. For this, the endoscopic white light images were divided into five equal-sized subsets. We avoided splitting the images of one patient into several subsets. A ResNet-50 model was trained with the images from four subsets and then validated with the remaining subset. This process was repeated for each subset, such that each subset was validated once. Sensitivity, specificity, and harmonic mean (F1) of the algorithm were determined.
Results
The algorithm showed values of 0.83, 0.88, and 0.84 for sensitivity, specificity, and F1, respectively. Further data showing a comparison between the detection rate of the AI model and that of experienced endoscopists will be available at the time of the upcoming conference.
Conclusions
We present the first clinical report on the use of a deep learning algorithm for the detection of celiac disease using endoscopic images. Further evaluation on an external data set, as well as in the detection of CD in real-time, will follow. However, this work at least suggests that AI can assist endoscopists in the endoscopic diagnosis of CD, and ultimately may be able to do a true optical biopsy in live-time.
esearch was and still is involved in the controversial issue about innate talent or extensive practice as the determinants of excellent performance in a range of domains. This study aims to contribute by presenting an analysis of practice activities in a domain that appears to be particularly suitable—orchestral conducting. Most conductors usually attain expertise in instrument playing prior to commencing conducting studies. Twenty-seven students of German study programs of orchestral conducting (approximately 18.7% of the population) responded to a questionnaire about their practice activities in conducting programs and their instrumental experiences. Descriptive results show the wealth of prestudy experiences conducting students have. A clear influence on practice activities cannot be stated. During study, students rated conducting-specific practice activities as more demanding and devoted more time to them than to general music practice activities. Therefore, conducting-specific practice activities might have been practiced more deliberately than general music practice activities.
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.
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.
As mobile technology continues to improve, more and more professional services are being offered as mobile apps. This paradigm shift also affects eHealth applications. Digital identities in nation-wide eHealth infrastructures are often realized via smart cards, which however, do not support mobile applications well. In this paper we propose a concept of a mobile eID for eHealth based on smartphones with embedded secure hardware, an mobile authenticator app and an account manager as well as an Identity Provider (IdP) as backend services. The practical applicability of the concept is shown using the example of the German eHealth infrastructure. Our method generates a cryptographic key pair in secure hardware on the user’s smartphone, registers it on the IdP and uses it to authenticate on the IdP. The security of the private key and the integrity of the smartphone is also validated and attestated. The user’s established smartcard-based identity "Electronic Health Card" (EHC) forms the trust anchor. To authenticate against specialist eHealth apps the IdP issues standard-compliant OAuth2.0/OIDC tokens with a limited period of validity. Furthermore, in our security analysis we demonstrate that based on specific security requirements for smartphones and operating systems, at least the eIDAS security level "substantial" related to the technical security aspects of the system can be achieved. On the basis of this research German legislation was adjusted and "digital identities" supplementary to the smartcard-based EHC will be issued from 2023 in the German eHealth infrastructure.
Ergonomic workplaces lead to fewer work-related musculoskeletal disorders and thus fewer sick days. There are various guidelines to help avoid harmful situations. However, these recommendations are often rather crude and often neglect the complex interaction of biomechanical loading and psychological stress. This study investigates whether machine learning algorithms can be used to predict mechanical and stress-related muscle activity for a standardized motion. For this purpose, experimental data were collected for trunk movement with and without additional psychological stress. Two different algorithms (XGBoost and TensorFlow) were used to model the experimental data. XGBoost in particular predicted the results very well. By combining it with musculoskeletal models, the method shown here can be used for workplace analysis but also for the development of real-time feedback systems in real workplace environments.
On closer examination, the concept of civil society becomes blurred, for instance, on the crucial question of who belongs to civil society. The different answers to this question show how controversial the concept of civil society is, since it cannot be assumed that members of civil society would be morally particularly preferable actors. If, however, the conceptual foundations of and the question of membership in civil society already raise doubts about this concept, the recourse to civil society as a means against fake news and hate speech itself, which is often heard in public debates, becomes doubtful.
Static pelvic tilt impacts functional cup position in total hip arthroplasty (THA). In the current study we investigated the effect of kinematic pelvic changes on cup position. In the course of a prospective controlled trial postoperative 3D-computed tomography (CT) and gait analysis before and 6 and 12 months after THA were obtained in 60 patients. Kinematic pelvic motion during gait was measured using Anybody Modeling System. By fusion with 3D-CT, the impact of kinematic pelvic tilt alterations on cup anteversion and inclination was calculated. Furthermore, risk factors correlating with high pelvic mobility were evaluated. During gait a high pelvic range of motion up to 15.6° exceeding 5° in 61.7% (37/60) of patients before THA was found. After surgery, the pelvis tilted posteriorly by a mean of 4.0 ± 6.6° (p < .001). The pelvic anteflexion led to a mean decrease of −1.9 ± 2.2° (p < .001) for cup inclination and −15.1 ± 6.1° (p < .001) for anteversion in relation to the anterior pelvic plane (APP). Kinematic pelvic changes resulted in a further change up to 2.3° for inclination and up to 12.3° for anteversion. In relation to the preoperative situation differences in postoperative cup position ranged from −4.4 to 4.6° for inclination and from −7.8 to 17.9° for anteversion, respectively. Female sex (p < .001) and normal body weight (p < .001) correlated with high alterations in pelvic tilt. Kinematic pelvic changes highly impact cup anteversion in THA. Surgeons using the APP as reference should aim for a higher anteversion of about 15° due to the functional anteflexion of the pelvis during gait.
Aim of the study was to develop a standardized model system to investigate endodontic irrigation techniques and assess the efficiency of different activation methods on the removal of hard tissue debris in complex root canal systems. Mesial roots of mandibular molars were firstly scanned by micro-computed tomography (µCT) and allocated to three groups of irrigant activation: sonic activation (EDDY, VDW, Munich, Germany), laser activation (AutoSWEEPS, FOTONA, Ljubljana, Slovenia) and conventional needle irrigation (control). Roots were fixed in individual 3D-printed holders to facilitate root canal enlargement under constant irrigation with NaOCl (5%). To enable standardized quantification of remaining debris, BaSO4-enriched dentine powder was compacted into the canals, followed by another µCT-scan. The final irrigation was performed using 17% ethylenediaminetetraacetic acid (EDTA) and 5% sodium hypochlorite (NaOCl) with the respective activation method, and the volume of remaining artificial debris was quantified after a final µCT-scan. The newly developed model system allowed for reliable, reproducible and standardized assessment of irrigation methods. Activation of the irrigant proved to be significantly more effective than conventional needle irrigation regarding the removal of debris, which persisted particularly in the apical third of the root canal in the control group. The efficiency of irrigation was significantly enhanced with laser- and sonic-based activation, especially in the apical third.