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Seit mehr als 25 Jahren ist der Workshop "Bildverarbeitung für die Medizin" als erfolgreiche Veranstaltung etabliert. Ziel ist auch 2024 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespräche zwischen Wissenschaftlern, Industrie und Anwendern. Die Beiträge dieses Bandes - viele davon in englischer Sprache - umfassen alle Bereiche der medizinischen Bildverarbeitung, insbesondere die Bildgebung und -akquisition, Segmentierung und Analyse, Visualisierung und Animation, computerunterstützte Diagnose sowie bildgestützte Therapieplanung und Therapie. Hierbei kommen Methoden des maschinelles Lernens, der biomechanischen Modellierung sowie der Validierung und Qualitätssicherung zum Einsatz.
BACKGROUND: Thrombosis remains a critical complication during venovenous extracorporeal membrane oxygenation (VV ECMO). The involvement of neutrophil extracellular traps (NETs) in thrombogenesis has to be discussed. The aim was to verify NETs in the form of cell-free DNA (cfDNA) in the plasma of patients during ECMO.
METHODS: A fluorescent DNA-binding dye (QuantifFluor®, Promega) was used to detect cell-free DNA in plasma samples. cfDNA concentrations from volunteers (n = 21) and patients (n = 9) were compared and correlated with clinical/technical data before/during support, ECMO end and time of a system exchange.
RESULTS: Before ECMO, patients with a median (IQR) age of 59 (51/63) years, SOFA score of 11 (10/15), and ECMO run time of 9.0 (7.0/19.5) days presented significantly higher levels of cfDNA compared to volunteers (6.4 (5.8/7.9) ng/μL vs. 5.9 (5.4/6.3) ng/μL; p = 0.044). Within 2 days after ECMO start, cfDNA, inflammatory, and hemolysis parameters remained unchanged, while platelets decreased (p = 0.005). After ECMO removal at the end of therapy, cfDNA, inflammation, and coagulation data (except antithrombin III) remained unchanged. The renewal of a system resulted in known alterations in fibrinogen, d-dimers, and platelets, while cfDNA remained unchanged.
CONCLUSION: Detection of cfDNA in plasma of ECMO patients was not an indicator of acute and circuit-induced thrombogenesis.
Aufgrund der steigenden Lebenserwartung und dem damit einhergehenden demographischen Wandel wird der Bedarf an Rehabilitations-Behandlungen in absehbarer Zukunft stark ansteigen. Ein Beispiel für diesen Trend ist die physiotherapeutische Behandlung nach Erhalt einer Knie-Totalendoprothese (Knie-TEP). So gehen Modellrechnungen basierend auf dem Bevölkerungswachstum und der bisherigen Prävalenz von Knie-TEPs davon aus, dass die Anzahl an durchgeführten Eingriffen in einkommensstarken Ländern wie Deutschland weiter zunehmen wird. Weiterhin stoßen traditionelle Rehabilitationsverfahren, gerade in strukturschwachen Regionen, schon heute an ihre Grenzen. Deutlich zu sehen war das während den Hochphasen der aktuellen Covid-19-Pandemie, als der Kontakt zwischen Therapeut*in und Patient*in flächendeckend eingeschränkt war. Eine erhöhte Nachfrage nach neuartigen Reha-Angeboten ist die logische Konsequenz. Innovative Konzepte sind daher dringend notwendig, um die daraus resultierenden technischen, sozialen und ökonomischen Herausforderungen zu bewältigen.
BACKGROUND:
Tracheobronchial mucus plays a crucial role in pulmonary function by providing protection against inhaled pathogens. Due to its composition of water, mucins, and other biomolecules, it has a complex viscoelastic rheological behavior. This interplay of both viscous and elastic properties has not been fully described yet. In this study, we characterize the rheology of human mucus using oscillatory and transient tests. Based on the transient tests, we describe the material behavior of mucus under stress and strain loading by mathematical models.
METHODS:
Mucus samples were collected from clinically used endotracheal tubes. For rheological characterization, oscillatory amplitude-sweep and frequency-sweep tests, and transient creep-recovery and stress-relaxation tests were performed. The results of the transient test were approximated using the Burgers model, the Weibull distribution, and the six-element Maxwell model. The three-dimensional microstructure of the tracheobronchial mucus was visualized using scanning electron microscope imaging.
RESULTS:
Amplitude-sweep tests showed storage moduli ranging from 0.1 Pa to 10000 Pa and a median critical strain of 4 %. In frequency-sweep tests, storage and loss moduli increased with frequency, with the median of the storage modulus ranging from 10 Pa to 30 Pa, and the median of the loss modulus from 5 Pa to 14 Pa. The Burgers model approximates the viscoelastic behavior of tracheobronchial mucus during a constant load of stress appropriately (R2 of 0.99), and the Weibull distribution is suitable to predict the recovery of the sample after the removal of this stress (R2 of 0.99). The approximation of the stress-relaxation test data by a six-element Maxwell model shows a larger fit error (R2 of 0.91).
CONCLUSIONS:
This study provides a detailed description of all process steps of characterizing the rheology of tracheobronchial mucus, including sample collection, microstructure visualization, and rheological investigation. Based on this characterization, we provide mathematical models of the rheological behavior of tracheobronchial mucus. These can now be used to simulate mucus flow in the respiratory system through numerical approaches.
To avoid dislocation of the shoulder joint after reverse total shoulder arthroplasty, it is important to achieve sufficient shoulder stability when placing the implant components during surgery. One parameter for assessing shoulder stability can be shoulder stiffness. The aim of this research was to develop a temporary reverse shoulder implant prototype that would allow intraoperative measurement of shoulder stiffness while varying the position of the implant components. Joint angle and torque measurement techniques were developed to determine shoulder stiffness. Hall sensors were used to measure the joint angles by converting the magnetic flux densities into angles. The accuracy of the joint angle measurements was tested using a test bench. Torques were determined by using thin-film pressure sensors. Various mechanical mechanisms for variable positioning of the implant components were integrated into the prototype. The results of the joint angle measurements showed measurement errors of less than 5° in a deflection range of ±15° adduction/abduction combined with ±45° flexion/extension. The proposed design provides a first approach for intra-operative assessment of shoulder stiffness. The findings can be used as a technological basis for further developments.
Seit mehr als 25 Jahren ist der Workshop "Bildverarbeitung für die Medizin" als erfolgreiche Veranstaltung etabliert. Ziel ist auch 2023 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespräche zwischen Wissenschaftlern, Industrie und Anwendern. Die Beiträge dieses Bandes - viele davon in englischer Sprache - umfassen alle Bereiche der medizinischen Bildverarbeitung, insbesondere die Bildgebung und -akquisition, Segmentierung und Analyse, Visualisierung und Animation, computerunterstützte Diagnose sowie bildgestützte Therapieplanung und Therapie. Hierbei kommen Methoden des maschinelles Lernens, der biomechanischen Modellierung sowie der Validierung und Qualitätssicherung zum Einsatz.
Surgical Smoke is generated during the cauterization of tissue with high-frequency (HF) devices and consists of 95% water vapor and 5% cellular debris. When the coagulation tweezers, which are supplied with HF voltage by the HF device, touch tissue, the electric circuit is closed, and smoke is generated by the heat. In-vivo investigations are performed during tracheotomies where surgical smoke is produced during coagulation of tissue. Furthermore, in-vitro parametric studies to investigate the particle number and size distribution and the spatial distribution of surgical smoke with laser light sheet technique are conducted. With higher power of the HF device, the particles generated are larger in size and the total number of particles generated is also higher. Adding artificial saliva to the tissue shows even higher particle counts. The study by laser light sheet also confirms this. The resulting characteristic size distribution, which may include viruses and bacterial components, confirms considering the risk arising from surgical smoke. Furthermore, the experiments will provide the database for further numerical investigations.
High Spatial Resolution Tomo-PIV of the Trachea Focussing on the Physiological Breathing Cycle
(2023)
Investigations of complex patient-specific flow in the nasopharynx requires high resolution numerical calculations validated by reliable experiments. When building the validation base and the benchmark of computational fluid dynamics, an experimental setup of the nasal airways was developed. The applied optical measurement technique of tomo-PIV supplies information on the governing flow field in three dimensions.
This paper presents tomo-PIV measurements of the highly complex patient-specific geometry of the human trachea. A computertomographic scan of a person’s head builds the basis of the experimental silicone model of the nasal airways. An optimised approach for precise refractive index matching avoids optical distortions even in highly complex non-free-of-sight 3D geometries. A linear-motor-driven pump generates breathing scenarios, based on measured breathing cycles. Adjusting of the CCD cameras‘ double-frame-rate PIV-Δt enables the detailed analysis of flow structures during different cycle phases. Merging regions of interest enables high spatial resolution acquisition of the flow field.
Für Mutter und Kind konnte das Risiko der Geburt durch die Weiterentwicklung der Medizin drastisch reduziert werden. Doch wie ist es um das Wohl derer bestellt, die die Gebärende unterstützen? Eine Studie der Ostbayerischen Technischen Hochschule Regensburg hat sich mit den muskuloskelettalen Beschwerden von Geburtshelfer:innen auseinandergesetzt
The special wing geometry of dragonflies consisting of veins and a membrane forming a corrugated profile leads to special aerodynamic characteristics. To capture the governing flow regimes of a dragonfly wing in detail, a realistic wing model has to be investigated. Therefore, this study aimed to analyze the aerodynamic characteristics of a 3D dragonfly wing reconstructed from a high-resolution micro-CT scan. Afterwards, a spatially high discretized mesh was generated using the mesh generator CENTAUR™ 14.5.0.2 (CentaurSoft, Austin, TX, US) to finally conduct Computational Fluid Dynamics (CFD) investigations in Fluent® 2020 R2 (ANSYS, Inc., Canonsburg, PA, US). Due to the small dimensions of the wing membrane, only the vein structure of a Camacinia Gigantea was captured at a micro-CT voxel size of 7 microns. The membrane was adapted and connected to the vein structure using a Boolean union operation. Occurring nconsistencies after combining the veins and the membrane were corrected using an adapted pymesh script [1]. As an initial study, only one quarter of the wing (outer wing section) was investigated to reduce the required computational effort. The resulting hybrid mesh consisting of 10 pseudo-structured prism layers along the wing surface and tetrahedra in the farfield area has 43 mio. nodes. The flow around the wing was considered to be incompressible and laminar using transient calculations. When the flow passes the vein structures, steady vortices occur in the corrugation valleys leading to recirculation zones. Therefore, the dragonfly wing resembles the profile of an airfoil. This leads to comparable lift coefficients of dragonfly wings and airfoil profiles at significantly reduced structural weight. The reconstructed geometry also included naturally occurring triangular prismlike serrated structures at the leading edge of the wing, which have comparable effects to micro vortex generators and might stabilize the recirculation zones. Further work aims to investigate the aerodynamic properties of a complete dragonfly wing during wing flapping.
High Spatial Resolution Tomo-PIV of the Nasopharynx Focussing on the Physiological Breathing Cycle
(2022)
Investigations of complex patient-specific flow in the nasopharynx requires high resolution numerical calculations validated by reliable experiments. When building the validation base and the benchmark
of computational fluid dynamics, an experimental setup of the nasal airways was developed. The applied optical measurement technique of tomo-PIV supplies information on the governing flow field in three dimensions. This paper presents tomo-PIV measurements of the highly complex patient-specific geometry of the human trachea. A computertomographic scan of a person’s head builds the basis of the experimental silicone model of the nasal airways. An optimised approach for precise refractive index matching avoids optical distortions even in highly complex non-free-of-sight 3D geometries. A linear-motor-driven pump generates breathing scenarios, based on measured breathing cycles. Adjusting of the CCD cameras‘ double-frame-rate PIV-Δt enables the detailed analysis of flow structures during different cycle phases. Merging regions of interest enables high spatial resolution acquisition of the flow field.
Surgical smoke has been a little discussed topic in the context of the current pandemic. Surgical smoke is generated during the cauterization of tissue with heat-generating devices and consists of 95% water
vapor and 5% cellular debris in the form of particulate matter. In-vivo investigations are performed during tracheotomies where surgical smoke is produced during tissue electrocautery. Furthermore, in-vitro parametric studies to investigate the particle number and size distribution and the spatial distribution of surgical smoke with laser light sheet technique are conducted. The higher the power of the high-frequency-device the larger the particles in size and the higher the resulting particle counts. The images taken show the densest smoke at 40W with artificial saliva. The resulting characteristic size distribution, which may include viruses and bacterial components, confirms that the risk arising from surgical smoke should be considered. Furthermore, the experiments will provide the database for further numerical investigations.
Clinical setting
Third space procedures such as endoscopic submucosal dissection (ESD) and peroral endoscopic myotomy (POEM) are complex minimally invasive techniques with an elevated risk for operator-dependent adverse events such as bleeding and perforation. This risk arises from accidental dissection into the muscle layer or through submucosal blood vessels as the submucosal cutting plane within the expanding resection site is not always apparent. Deep learning algorithms have shown considerable potential for the detection and characterization of gastrointestinal lesions. So-called AI – clinical decision support solutions (AI-CDSS) are commercially available for polyp detection during colonoscopy. Until now, these computer programs have concentrated on diagnostics whereas an AI-CDSS for interventional endoscopy has not yet been introduced. We aimed to develop an AI-CDSS („Smart ESD“) for real-time intra-procedural detection and delineation of blood vessels, tissue structures and endoscopic instruments during third-space endoscopic procedures.
Characteristics of Smart ESD
An AI-CDSS was invented that delineates blood vessels, tissue structures and endoscopic instruments during third-space endoscopy in real-time. The output can be displayed by an overlay over the endoscopic image with different modes of visualization, such as a color-coded semitransparent area overlay, or border tracing (demonstration video). Hereby the optimal layer for dissection can be visualized, which is close above or directly at the muscle layer, depending on the applied technique (ESD or POEM). Furthermore, relevant blood vessels (thickness> 1mm) are delineated. Spatial proximity between the electrosurgical knife and a blood vessel triggers a warning signal. By this guidance system, inadvertent dissection through blood vessels could be averted.
Technical specifications
A DeepLabv3+ neural network architecture with KSAC and a 101-layer ResNeSt backbone was used for the development of Smart ESD. It was trained and validated with 2565 annotated still images from 27 full length third-space endoscopic videos. The annotation classes were blood vessel, submucosal layer, muscle layer, electrosurgical knife and endoscopic instrument shaft. A test on a separate data set yielded an intersection over union (IoU) of 68%, a Dice Score of 80% and a pixel accuracy of 87%, demonstrating a high overlap between expert and AI segmentation. Further experiments on standardized video clips showed a mean vessel detection rate (VDR) of 85% with values of 92%, 70% and 95% for POEM, rectal ESD and esophageal ESD respectively. False positive measurements occurred 0.75 times per minute. 7 out of 9 vessels which caused intraprocedural bleeding were caught by the algorithm, as well as both vessels which required hemostasis via hemostatic forceps.
Future perspectives
Smart ESD performed well for vessel and tissue detection and delineation on still images, as well as on video clips. During a live demonstration in the endoscopy suite, clinical applicability of the innovation was examined. The lag time for processing of the live endoscopic image was too short to be visually detectable for the interventionist. Even though the algorithm could not be applied during actual dissection by the interventionist, Smart ESD appeared readily deployable during visual assessment by ESD experts. Therefore, we plan to conduct a clinical trial in order to obtain CE-certification of the algorithm. This new technology may improve procedural safety and speed, as well as training of modern minimally invasive endoscopic resection techniques.
Autologous lipotransfer is a promising method for tissue regeneration, because white adipose tissue contains a heterogeneous cell population, including mesenchymal stem cells, endothelial cells, immune cells, and adipocytes. In order to improve the outcome, adipose tissue can be processed before application. In this study, we investigated changes caused by mechanical processing.
Lipoaspirates were processed using sedimentation, first-time centrifugation, shear-force homogenization, and second-time centrifugation. The average adipocyte size, stromal vascular cell count, and adipocyte depot size were examined histologically at every processing step. In addition, the adipose derived stem cells (ADSCs) were isolated and differentiated osteogenically and adipogenically. While homogenization causes a disruption of adipocyte depots, the shape of the remaining adipocytes is not changed. On average, these adipocytes are smaller than the depot adipocytes, they are surrounded by the ECM, and therefore mechanically more stable. The volume loss of adipocyte depots leads to a significant enrichment of stromal vascular cells such as ADSCs. However, the mechanical processing does not change the potential of the ADSCs to differentiate adipogenically or steogenically. It thus appears that mechanically processed lipoaspirates are promising for the reparation of even mechanically stressed tissue as that found in nasolabial folds. The changes resulting from the processing correspond more to a filtration of mechanically less stable components than to a manipulation of the tissue.
Convolutional Neural Networks for Approximation of Internal Non-Newtonian Multiphase Flow Fields
(2021)
Neural networks (NNs) as an alternative method for universal approximation of differential equations have proven to be computationally efficient and still sufficiently accurate compared to established methods such as the finite volume method (FVM). Additionally, analysing weights and biases can give insights into the underlying physical laws. FVM and NNs are both based upon spacial discretisation. Since a Cartesian and equidistant grid is a raster graphics, image-to-image regression techniques can be used to predict phase velocity fields as well as particle and pressure distributions from simple mass flow boundary conditions. The impact of convolution layer depth and number of channels of a ConvolutionDeconvolution Regression Network (CDRN), on prediction performance of internal non-Newtownian multiphase flows is investigated. Parametric training data with 2055 sets is computed using FVM. To capture significant non-Newtownian effects of a particle-laden fluid (e.g. blood) flowing through small and non-straight channels, an Euler-Euler multiphase approach is used. The FVM results are normalized and mapped onto an equidistant grid as supervised learning target. The investigated NNs consist of n= {3, 5, 7} corresponding encoding/decoding blocks and different skip connections. Regardless of the convolution depth (i.e. number of blocks), the deepest spacial down-sampling via strided convolution is adjusted to result in a 1 × 1 × f · 2nfeature map, with f = {8, 16, 32}. The prediction performance expressed is as channel-averaged normalized root mean squared error (NRMSE). With a NRMSE of < 2 · 10-3, the best preforming NN has f = 32 initial feature maps, a kernel size of k = 4, n = 5 blocks and dense skip connections. Average inference time from this NN takes < 7 · 10-3s. Worst accuracy at NRMSE of approx 9 · 10-3is achieved without any skips, at k = 2, f = 16 and n = 3, but deployment takes only < 2 · 10-3s Given an adequate training, the prediction accuracy improves with convolution depth, where more features have higher impact on deeper NNs. Due to skip connections and batch normalisation, training is similarly efficient, regardless of the depth. This is further improved by blocks with dense connections, but at the price of a drastically larger model. Depending on geometrical complexity, spacial resolution is critical, as it increases the number of learnables and memory requirements massively.
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.
Resting motor threshold and magnetic field output of the figure-of-8 and the double-cone coil
(2020)
The use of the double-cone (DC) coil in transcranial magnetic stimulation (TMS) is promoted with the notion that the DC coil enables stimulation of deeper brain areas in contrast to conventional figure-of-8 (Fo8) coils. However, systematic comparisons of these two coil types with respect to the spatial distribution of the magnetic field output and also to the induced activity in superficial and deeper brain areas are limited. Resting motor thresholds of the left and right first dorsal interosseous (FDI) and tibialis anterior (TA) were determined with the DC and the Fo8 coil in 17 healthy subjects. Coils were orientated over the corresponding motor area in an angle of 45 degrees for the hand area with the handle pointing in posterior direction and in medio-lateral direction for the leg area. Physical measurements were done with an automatic gantry table using a Gaussmeter. Resting motor threshold was higher for the leg area in contrast to the hand area and for the Fo8 in contrast to the DC coil. Muscle by coil interaction was also significant providing higher differences between leg and hand area for the Fo8 (about 27%) in contrast to the DC coil (about 15%). Magnetic field strength was higher for the DC coil in contrast to the Fo8 coil. The DC coil produces a higher magnetic field with higher depth of penetration than the figure of eight coil.
BACKGROUND:
Due to their corrugated profile, dragonfly wings have special aerodynamic characteristics during flying and gliding. OBJECTIVE: The aim of this study was to create a realistic 3D model of a dragonfly wing captured with a high-resolution micro-CT. To represent geometry changes in span and chord length and their aerodynamic effects, numerical investigations are carried out at different wing positions. METHODS:
The forewing of a Camacinia gigantea was captured using a micro-CT. After the wing was adapted an error-free 3D model resulted. The wing was cut every 5 mm and 2D numerical analyses were conducted in Fluent® 2020 R2 (ANSYS, Inc., Canonsburg, PA, USA). RESULTS: The highest lift coefficient, as well as the highest lift-to-drag ratio, resulted at 0 mm and an angle of attack (AOA) of 5∘. At AOAs of 10∘ or 15∘, the flow around the wing stalled and a Kármán vortex street behind the wing becomes
CONCLUSIONS:
The velocity is higher on the upper side of the wing compared to the lower side. The pressure acts vice versa. Due to the recirculation zones that are formed in valleys of the corrugation pattern the wing resembles the form of an airfoil.
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.
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.
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.
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.
In den letzten Jahren hat sich der Workshop "Bildverarbeitung für die Medizin" durch erfolgreiche Veranstaltungen etabliert. Ziel ist auch 2021 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespräche zwischen Wissenschaftlern, Industrie und Anwendern. Die Beiträge dieses Bandes - einige davon in englischer Sprache - umfassen alle Bereiche der medizinischen Bildverarbeitung, insbesondere Bildgebung und -akquisition, Maschinelles Lernen, Bildsegmentierung und Bildanalyse, Visualisierung und Animation, Zeitreihenanalyse, Computerunterstützte Diagnose, Biomechanische Modellierung, Validierung und Qualitätssicherung, Bildverarbeitung in der Telemedizin u.v.m.
Die fortschreitende Digitalisierung des Gesundheitswesens ebnet auch den Weg einer stärkeren Verbreitung der telemedizinischen Versorgung in Deutschland. Die Politik erleichtert diesen Weg, z.B. mit dem eHealth-Gesetz oder mit der Lockerung des "Fernbehandlungsverbots" durch den Deutschen Ärztetag. Dabei wird schon aus der Definition (siehe z.B. die unten stehende Definition der AG Telemedizin) klar, dass Telemedizin keine neue medizinische Disziplin begründet. Bei Telemedizin handelt es sich nur um ein technisches Werkzeug in Kombination mit geeigneten klinischen Prozessen, damit bestehende medizinische Expertise unter Einsatz von Informations- und Kommunikationstechnologien räumliche Entfernungen oder einen zeitlichen Versatz überbrücken kann. Patientinnen und Patienten können also behandelt werden, auch wenn sie vom Arzt räumlich entfernt sind.