TY - GEN A1 - Ebigbo, Alanna A1 - Rauber, David A1 - Ayoub, Mousa A1 - Birzle, Lisa A1 - Matsumura, Tomoaki A1 - Probst, Andreas A1 - Steinbrück, Ingo A1 - Nagl, Sandra A1 - Römmele, Christoph A1 - Meinikheim, Michael A1 - Scheppach, Markus W. A1 - Palm, Christoph A1 - Messmann, Helmut T1 - Early Esophageal Cancer and the Generalizability of Artificial Intelligence T2 - Endoscopy N2 - Aims Artificial Intelligence (AI) systems in gastrointestinal endoscopy are narrow because they are trained to solve only one specific task. Unlike Narrow-AI, general AI systems may be able to solve multiple and unrelated tasks. We aimed to understand whether an AI system trained to detect, characterize, and segment early Barrett’s neoplasia (Barrett’s AI) is only capable of detecting this pathology or can also detect and segment other diseases like early squamous cell cancer (SCC). Methods 120 white light (WL) and narrow-band endoscopic images (NBI) from 60 patients (1 WL and 1 NBI image per patient) were extracted from the endoscopic database of the University Hospital Augsburg. Images were annotated by three expert endoscopists with extensive experience in the diagnosis and endoscopic resection of early esophageal neoplasias. An AI system based on DeepLabV3+architecture dedicated to early Barrett’s neoplasia was tested on these images. The AI system was neither trained with SCC images nor had it seen the test images prior to evaluation. The overlap between the three expert annotations („expert-agreement“) was the ground truth for evaluating AI performance. Results Barrett’s AI detected early SCC with a mean intersection over reference (IoR) of 92% when at least 1 pixel of the AI prediction overlapped with the expert-agreement. When the threshold was increased to 5%, 10%, and 20% overlap with the expert-agreement, the IoR was 88%, 85% and 82%, respectively. The mean Intersection Over Union (IoU) – a metric according to segmentation quality between the AI prediction and the expert-agreement – was 0.45. The mean expert IoU as a measure of agreement between the three experts was 0.60. Conclusions In the context of this pilot study, the predictions of SCC by a Barrett’s dedicated AI showed some overlap to the expert-agreement. Therefore, features learned from Barrett’s cancer-related training might be helpful also for SCC prediction. Our results allow different possible explanations. On the one hand, some Barrett’s cancer features generalize toward the related task of assessing early SCC. On the other hand, the Barrett’s AI is less specific to Barrett’s cancer than a general predictor of pathological tissue. However, we expect to enhance the detection quality significantly by extending the training to SCC-specific data. The insight of this study opens the way towards a transfer learning approach for more efficient training of AI to solve tasks in other domains. Y1 - 2024 U6 - https://doi.org/10.1055/s-0044-1783775 VL - 56 IS - S 02 SP - S428 PB - Thieme CY - Stuttgart ER - TY - GEN A1 - Scheppach, Markus W. A1 - Mendel, Robert A1 - Rauber, David A1 - Probst, Andreas A1 - Nagl, Sandra A1 - Römmele, Christoph A1 - Meinikheim, Michael A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Artificial Intelligence (AI) improves endoscopists’ vessel detection during endoscopic submucosal dissection (ESD) T2 - Endoscopy N2 - Aims While AI has been successfully implemented in detecting and characterizing colonic polyps, its role in therapeutic endoscopy remains to be elucidated. Especially third space endoscopy procedures like ESD and peroral endoscopic myotomy (POEM) pose a technical challenge and the risk of operator-dependent complications like intraprocedural bleeding and perforation. Therefore, we aimed at developing an AI-algorithm for intraprocedural real time vessel detection during ESD and POEM. Methods A training dataset consisting of 5470 annotated still images from 59 full-length videos (47 ESD, 12 POEM) and 179681 unlabeled images was used to train a DeepLabV3+neural network with the ECMT semi-supervised learning method. Evaluation for vessel detection rate (VDR) and time (VDT) of 19 endoscopists with and without AI-support was performed using a testing dataset of 101 standardized video clips with 200 predefined blood vessels. Endoscopists were stratified into trainees and experts in third space endoscopy. Results The AI algorithm had a mean VDR of 93.5% and a median VDT of 0.32 seconds. AI support was associated with a statistically significant increase in VDR from 54.9% to 73.0% and from 59.0% to 74.1% for trainees and experts, respectively. VDT significantly decreased from 7.21 sec to 5.09 sec for trainees and from 6.10 sec to 5.38 sec for experts in the AI-support group. False positive (FP) readings occurred in 4.5% of frames. FP structures were detected significantly shorter than true positives (0.71 sec vs. 5.99 sec). Conclusions AI improved VDR and VDT of trainees and experts in third space endoscopy and may reduce performance variability during training. Further research is needed to evaluate the clinical impact of this new technology. Y1 - 2024 U6 - https://doi.org/10.1055/s-0044-1782891 VL - 56 IS - S 02 SP - S93 PB - Thieme CY - Stuttgart ER - TY - GEN A1 - Zellmer, Stephan A1 - Rauber, David A1 - Probst, Andreas A1 - Weber, Tobias A1 - Braun, Georg A1 - Römmele, Christoph A1 - Nagl, Sandra A1 - Schnoy, Elisabeth A1 - Messmann, Helmut A1 - Ebigbo, Alanna A1 - Palm, Christoph T1 - Artificial intelligence as a tool in the detection of the papillary ostium during ERCP T2 - Endoscopy N2 - Aims Endoscopic retrograde cholangiopancreaticography (ERCP) is the gold standard in the diagnosis as well as treatment of diseases of the pancreatobiliary tract. However, it is technically complex and has a relatively high complication rate. In particular, cannulation of the papillary ostium remains challenging. The aim of this study is to examine whether a deep-learning algorithm can be used to detect the major duodenal papilla and in particular the papillary ostium reliably and could therefore be a valuable tool for inexperienced endoscopists, particularly in training situation. Methods We analyzed a total of 654 retrospectively collected images of 85 patients. Both the major duodenal papilla and the ostium were then segmented. Afterwards, a neural network was trained using a deep-learning algorithm. A 5-fold cross-validation was performed. Subsequently, we ran the algorithm on 5 prospectively collected videos of ERCPs. Results 5-fold cross-validation on the 654 labeled data resulted in an F1 value of 0.8007, a sensitivity of 0.8409 and a specificity of 0.9757 for the class papilla, and an F1 value of 0.5724, a sensitivity of 0.5456 and a specificity of 0.9966 for the class ostium. Regardless of the class, the average F1 value (class papilla and class ostium) was 0.6866, the sensitivity 0.6933 and the specificity 0.9861. In 100% of cases the AI-detected localization of the papillary ostium in the prospectively collected videos corresponded to the localization of the cannulation performed by the endoscopist. Conclusions In the present study, the neural network was able to identify the major duodenal papilla with a high sensitivity and high specificity. In detecting the papillary ostium, the sensitivity was notably lower. However, when used on videos, the AI was able to identify the location of the subsequent cannulation with 100% accuracy. In the future, the neural network will be trained with more data. Thus, a suitable tool for ERCP could be established, especially in the training situation. Y1 - 2024 U6 - https://doi.org/10.1055/s-0044-1783138 VL - 56 IS - S 02 SP - S198 PB - Thieme CY - Stuttgart ER - TY - GEN A1 - Scheppach, Markus W. A1 - Nunes, Danilo Weber A1 - Arizi, X. A1 - Rauber, David A1 - Probst, Andreas A1 - Nagl, Sandra A1 - Römmele, Christoph A1 - Meinikheim, Michael A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Procedural phase recognition in endoscopic submucosal dissection (ESD) using artificial intelligence (AI) T2 - Endoscopy N2 - Aims Recent evidence suggests the possibility of intraprocedural phase recognition in surgical operations as well as endoscopic interventions such as peroral endoscopic myotomy and endoscopic submucosal dissection (ESD) by AI-algorithms. The intricate measurement of intraprocedural phase distribution may deepen the understanding of the procedure. Furthermore, real-time quality assessment as well as automation of reporting may become possible. Therefore, we aimed to develop an AI-algorithm for intraprocedural phase recognition during ESD. Methods A training dataset of 364385 single images from 9 full-length ESD videos was compiled. Each frame was classified into one procedural phase. Phases included scope manipulation, marking, injection, application of electrical current and bleeding. Allocation of each frame was only possible to one category. This training dataset was used to train a Video Swin transformer to recognize the phases. Temporal information was included via logarithmic frame sampling. Validation was performed using two separate ESD videos with 29801 single frames. Results The validation yielded sensitivities of 97.81%, 97.83%, 95.53%, 85.01% and 87.55% for scope manipulation, marking, injection, electric application and bleeding, respectively. Specificities of 77.78%, 90.91%, 95.91%, 93.65% and 84.76% were measured for the same parameters. Conclusions The developed algorithm was able to classify full-length ESD videos on a frame-by-frame basis into the predefined classes with high sensitivities and specificities. Future research will aim at the development of quality metrics based on single-operator phase distribution. Y1 - 2024 U6 - https://doi.org/10.1055/s-0044-1783804 VL - 56 IS - S 02 SP - S439 PB - Thieme CY - Stuttgart ER - TY - GEN A1 - Scheppach, Markus W. A1 - Rauber, David A1 - Stallhofer, Johannes A1 - Muzalyova, Anna A1 - Otten, Vera A1 - Manzeneder, Carolin A1 - Schwamberger, Tanja A1 - Wanzl, Julia A1 - Schlottmann, Jakob A1 - Tadic, Vidan A1 - Probst, Andreas A1 - Schnoy, Elisabeth A1 - Römmele, Christoph A1 - Fleischmann, Carola A1 - Meinikheim, Michael A1 - Miller, Silvia A1 - Märkl, Bruno A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Performance comparison of a deep learning algorithm with endoscopists in the detection of duodenal villous atrophy (VA) T2 - Endoscopy N2 - Aims  VA is an endoscopic finding of celiac disease (CD), which can easily be missed if pretest probability is low. In this study, we aimed to develop an artificial intelligence (AI) algorithm for the detection of villous atrophy on endoscopic images. Methods 858 images from 182 patients with VA and 846 images from 323 patients with normal duodenal mucosa were used for training and internal validation of an AI algorithm (ResNet18). A separate dataset was used for external validation, as well as determination of detection performance of experts, trainees and trainees with AI support. According to the AI consultation distribution, images were stratified into “easy” and “difficult”. Results Internal validation showed 82%, 85% and 84% for sensitivity, specificity and accuracy. External validation showed 90%, 76% and 84%. The algorithm was significantly more sensitive and accurate than trainees, trainees with AI support and experts in endoscopy. AI support in trainees was associated with significantly improved performance. While all endoscopists showed significantly lower detection for “difficult” images, AI performance remained stable. Conclusions The algorithm outperformed trainees and experts in sensitivity and accuracy for VA detection. The significant improvement with AI support suggests a potential clinical benefit. Stable performance of the algorithm in “easy” and “difficult” test images may indicate an advantage in macroscopically challenging cases. Y1 - 2023 U6 - https://doi.org/10.1055/s-0043-1765421 VL - 55 IS - S02 PB - Thieme ER - TY - GEN A1 - Scheppach, Markus W. A1 - Weber Nunes, Danilo A1 - Arizi, X. A1 - Rauber, David A1 - Probst, Andreas A1 - Nagl, Sandra A1 - Römmele, Christoph A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Single frame workflow recognition during endoscopic submucosal dissection (ESD) using artificial intelligence (AI) T2 - Endoscopy N2 - Aims  Precise surgical phase recognition and evaluation may improve our understanding of complex endoscopic procedures. Furthermore, quality control measurements and endoscopy training could benefit from objective descriptions of surgical phase distributions. Therefore, we aimed to develop an artificial intelligence algorithm for frame-by-frame operational phase recognition during endoscopic submucosal dissection (ESD). Methods  Full length ESD-videos from 31 patients comprising 6.297.782 single images were collected retrospectively. Videos were annotated on a frame-by-frame basis for the operational macro-phases diagnostics, marking, injection, dissection and bleeding. Further subphases were the application of electrical current, visible injection of fluid into the submucosal space and scope manipulation, leading to 11 phases in total. 4.975.699 frames (21 patients) were used for training of a video swin transformer using uniform frame sampling for temporal information. Hyperparameter tuning was performed with 897.325 further frames (6 patients), while 424.758 frames (4 patients) were used for validation. Results  The overall F1 scores on the test dataset for the macro-phases and all 11 phases were 0.96 and 0.90, respectively. The recall values for diagnostics, marking, injection, dissection and bleeding were 1.00, 1.00, 0.95, 0.96 and 0.93, respectively. Conclusions  The algorithm classified operational phases during ESD with high accuracy. A precise evaluation of phase distribution may allow for the development of objective quality metrics for quality control and training. Y1 - 2025 U6 - https://doi.org/10.1055/s-0045-1806324 VL - 57 IS - S 02 SP - S511 PB - Thieme CY - Stuttgart ER - TY - GEN A1 - Roser, David A1 - Meinikheim, Michael A1 - Mendel, Robert A1 - Palm, Christoph A1 - Probst, Andreas A1 - Muzalyova, Anna A1 - Scheppach, Markus W. A1 - Nagl, Sandra A1 - Schnoy, Elisabeth A1 - Römmele, Christoph A1 - Schulz, Dominik Andreas Helmut Otto A1 - Schlottmann, Jakob A1 - Prinz, Friederike A1 - Rauber, David A1 - Rückert, Tobias A1 - Matsumura, Tomoaki A1 - Fernandez-Esparrach, G. A1 - Parsa, Nasim A1 - Byrne, Michael F. A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Human-Computer Interaction: Impact of Artificial Intelligence on the diagnostic confidence of endoscopists assessing videos of Barrett’s esophagus T2 - Endoscopy N2 - Aims Human-computer interactions (HCI) may have a relevant impact on the performance of Artificial Intelligence (AI). Studies show that although endoscopists assessing Barrett’s esophagus (BE) with AI improve their performance significantly, they do not achieve the level of the stand-alone performance of AI. One aspect of HCI is the impact of AI on the degree of certainty and confidence displayed by the endoscopist. Indirectly, diagnostic confidence when using AI may be linked to trust and acceptance of AI. In a BE video study, we aimed to understand the impact of AI on the diagnostic confidence of endoscopists and the possible correlation with diagnostic performance. Methods 22 endoscopists from 12 centers with varying levels of BE experience reviewed ninety-six standardized endoscopy videos. Endoscopists were categorized into experts and non-experts and randomly assigned to assess the videos with and without AI. Participants were randomized in two arms: Arm A assessed videos first without AI and then with AI, while Arm B assessed videos in the opposite order. Evaluators were tasked with identifying BE-related neoplasia and rating their confidence with and without AI on a scale from 0 to 9. Results The utilization of AI in Arm A (without AI first, with AI second) significantly elevated confidence levels for experts and non-experts (7.1 to 8.0 and 6.1 to 6.6, respectively). Only non-experts benefitted from AI with a significant increase in accuracy (68.6% to 75.5%). Interestingly, while the confidence levels of experts without AI were higher than those of non-experts with AI, there was no significant difference in accuracy between these two groups (71.3% vs. 75.5%). In Arm B (with AI first, without AI second), experts and non-experts experienced a significant reduction in confidence (7.6 to 7.1 and 6.4 to 6.2, respectively), while maintaining consistent accuracy levels (71.8% to 71.8% and 67.5% to 67.1%, respectively). Conclusions AI significantly enhanced confidence levels for both expert and non-expert endoscopists. Endoscopists felt significantly more uncertain in their assessments without AI. Furthermore, experts with or without AI consistently displayed higher confidence levels than non-experts with AI, irrespective of comparable outcomes. These findings underscore the possible role of AI in improving diagnostic confidence during endoscopic assessment. Y1 - 2024 U6 - https://doi.org/10.1055/s-0044-1782859 SN - 1438-8812 VL - 56 IS - S 02 SP - 79 PB - Georg Thieme Verlag ER - TY - GEN A1 - Zellmer, Stephan A1 - Rauber, David A1 - Probst, Andreas A1 - Weber, Tobias A1 - Braun, Georg A1 - Nagl, Sandra A1 - Römmele, Christoph A1 - Schnoy, Elisabeth A1 - Birzle, Lisa A1 - Aehling, Niklas A1 - Schulz, Dominik Andreas Helmut Otto A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Künstliche Intelligenz als Hilfsmittel zur Detektion der Papilla duodeni major und des papillären Ostiums während der ERCP T2 - Zeitschrift für Gastroenterologie N2 - Einleitung  Die Endoskopische Retrograde Cholangiopankreatikographie (ERCP) ist der Goldstandard in der endoskopischen Therapie von Erkrankungen des pankreatobiliären Trakts. Allerdings ist sie technisch anspruchsvoll, schwer zu erlernen und mit einer relativ hohen Komplikationsrate assoziiert. Daher soll in der vorliegenden Machbarkeitsstudie geprüft werden, ob mithilfe eines Deeplearning- Algorithmus die Papille und das Ostium zuverlässig detektiert werden können und dieser für Endoskopiker, insbesondere in der Ausbildungssituation, ein geeignetes Hilfsmittel darstellen könnte. Material und Methodik Insgesamt wurden 1534 ERCP-Bilder von 134 Patienten analysiert, wobei sowohl die Papilla duodeni major als auch das Ostium segmentiert wurden. Anschließend erfolgte das Training eines neuronalen Netzes unter Verwendung eines Deep-Learning-Algorithmus. Für den Test des Algorithmus erfolgte eine fünffache Kreuzvalidierung. Ergebnisse  Auf den 1534 gelabelten Bildern wurden für die Klasse Papille ein F1-Wert von 0,7996, eine Sensitivität von 0,8488 und eine Spezifität von 0,9822 erzielt. Für die Klasse Ostium ergaben sich ein F1-Wert von 0,5198, eine Sensitivität von 0,5945 und eine Spezifität von 0,9974. Klassenübergreifend (Klasse Papille und Klasse Ostium) betrug der F1-Wert 0,6593, die Sensitivität 0,7216 und für die Spezifität 0,9898. Zusammenfassung  In der vorliegenden Machbarkeitsstudie zeigte das neuronale Netz eine hohe Sensitivität und eine sehr hohe Spezifität bei der Identifikation der Papilla duodeni major. Die Detektion des Ostiums erfolgte hingegen mit einer deutlich geringeren Sensitivität. Zukünftig ist eine Erweiterung des Trainingsdatensatzes um Videos und klinische Daten vorgesehen, um die Leistungsfähigkeit des Netzwerks zu verbessern. Hierdurch könnte langfristig ein geeignetes Assistenzsystem für die ERCP, insbesondere in der Ausbildungssituation etabliert werden. Y1 - 2025 U6 - https://doi.org/10.1055/s-0045-1806882 VL - 63 IS - 5 SP - e295 PB - Thieme CY - Stuttgart ER - TY - GEN A1 - Bauer, Dagmar A1 - Stoffels, Gabriele A1 - Pauleit, Dirk A1 - Palm, Christoph A1 - Hamacher, Kurt A1 - Coenen, Heinz H. A1 - Langen, Karl T1 - Uptake of F-18-fluoroethyl-L-tyrosine and H-3-L-methionine in focal cortical ischemia T2 - The Journal of Nuclear Medicine N2 - 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. Y1 - 2006 UR - http://jnm.snmjournals.org/content/47/suppl_1/284P.3 VL - 47 IS - Suppl. 1 SP - 284P ER - TY - GEN A1 - Beyer, Thomas A1 - Weigert, Markus A1 - Palm, Christoph A1 - Quick, Harald H. A1 - Müller, Stefan P. A1 - Pietrzyk, Uwe A1 - Vogt, Florian A1 - Martinez, M.J. A1 - Bockisch, Andreas T1 - Towards MR-based attenuation correction for whole-body PET/MR imaging T2 - The Journal of Nuclear Medicine KW - Kernspintomografie KW - Positronen-Emissions-Tomografie KW - Bildgebendes Verfahren KW - Schwächung Y1 - 2006 UR - http://jnm.snmjournals.org/content/47/suppl_1/384P.1.abstract VL - 47 IS - Suppl. 1 SP - 384P ER - TY - GEN A1 - Gräßel, David A1 - Axer, Markus A1 - Palm, Christoph A1 - Dammers, Jürgen A1 - Amunts, Katrin A1 - Pietrzyk, Uwe A1 - Zilles, Karl T1 - Visualization of Fiber Tracts in the Postmortem Human Brain by Means of Polarized Light T2 - NeuroImage KW - Gehirn KW - Bildgebendes Verfahren KW - Polarisiertes Licht KW - Pathologische Anatomie Y1 - 2009 U6 - https://doi.org/10.1016/S1053-8119(09)71415-6 VL - 47 IS - Suppl. 1 SP - 142 ER - TY - GEN A1 - Weigert, Markus A1 - Beyer, Thomas A1 - Quick, Harald H. A1 - Pietrzyk, Uwe A1 - Palm, Christoph A1 - Müller, Stefan P. T1 - Generation of a MRI reference data set for the validation of automatic, non-rigid image co-registration algorithms T2 - Nuklearmedizin KW - Kernspintomografie KW - Referenzdaten KW - Registrierung KW - Algorithmus Y1 - 2007 VL - 46 IS - 2 SP - A116 ER - TY - GEN A1 - Weigert, Markus A1 - Palm, Christoph A1 - Quick, Harald H. A1 - Müller, Stefan P. A1 - Pietrzyk, Uwe A1 - Beyer, Thomas T1 - Template for MR-based attenuation correction for whole-body PET/MR imaging T2 - Nuklearmedizin KW - Kernspintomografie KW - Positronen-Emissions-Tomografie KW - Bildgebendes Verfahren KW - Schwächung Y1 - 2007 VL - 46 IS - 2 SP - A115 ER - TY - GEN A1 - Axer, Markus A1 - Axer, Hubertus A1 - Palm, Christoph A1 - Gräßel, David A1 - Zilles, Karl A1 - Pietrzyk, Uwe T1 - Visualization of Nerve Fibre Orientation in the Visual Cortex of the Human Brain by Means of Polarized Light T2 - Biomedizinische Technik KW - Sehrinde KW - Nervenfaser KW - Ausrichtung KW - Visualisierung KW - Polarisiertes Licht Y1 - 2007 VL - 52 IS - Suppl. SP - 1569048-041 ER - TY - GEN A1 - Palm, Christoph A1 - Crum, William R. A1 - Pietrzyk, Uwe A1 - Hawkes, David J. T1 - Application of Fluid and Elastic Registration Methods to Histological Rat Brain Sections T2 - Biomedizinische Technik KW - Registrierung KW - Gehirn KW - Schnittdarstellung Y1 - 2007 VL - 52 IS - Suppl. ER - TY - GEN A1 - Ebigbo, Alanna A1 - Mendel, Robert A1 - Probst, Andreas A1 - Manzeneder, Johannes A1 - Souza Jr., Luis Antonio de A1 - Papa, João Paulo A1 - Palm, Christoph A1 - Messmann, Helmut T1 - Artificial Intelligence in Early Barrett's Cancer: The Segmentation Task T2 - Endoscopy N2 - 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. KW - Speiseröhrenkrankheit KW - Maschinelles Lernen KW - Barrett's esphagus KW - Deep Learning KW - Segmentation Y1 - 2019 U6 - https://doi.org/10.1055/s-0039-1681187 VL - 51 IS - 04 SP - 6 PB - Georg Thieme Verlag CY - Stuttgart ER - TY - GEN A1 - Krefting, Dagmar A1 - Zaunseder, Sebastian A1 - Säring, Dennis A1 - Wittenberg, Thomas A1 - Palm, Christoph A1 - Schiecke, Karin A1 - Krenkel, Lars A1 - Hennemuth, Anja A1 - Schnell, Susanne A1 - Spicher, Nicolai T1 - Blutdruck, Hämodynamik und Gefäßzustand: Innovative Erfassung und Bewertung – Schwerpunkt bildbasierte Verfahren T2 - 66. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 12. Jahreskongress der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e. V. (TMF), 26. - 30.09.2021, online N2 - Einleitung: Blutdruck gilt als sogenannter Vitalparameter als einer der grundlegenden Indikatoren für den Gesundheitszustand einer Person. Sowohl zu niedriger als auch zu hoher Blutdruck kann lebensbedrohend sein, letzerer ist darüber hinaus ein Risikofaktor insbesondere für Herz-Kreislauferkrankungen, die trotz wichtiger Fortschritte in der Behandlung immer noch die häufigste Todesursache in Deutschland darstellen. Die Hämodynamik, also die raumzeitliche Dynamik des Blutflusses, und der Gefäßzustand sind eng verbunden mit dem Blutdruck und ebenfalls von hoher klinischer Relevanz, u.a. zur Identifikation von Durchblutungsstörungen und ungünstigen Druckverteilungen der Gefäßwand. Innovationen in der Messtechnik als auch in der Datenanalyse bieten heute neue Möglichkeiten der Erfassung und Bewertung von Blutdruck, Hämodynamik und Gefäßzustand [1], [2], [3], [4]. Methodik: In einer gemeinsamen Workshopserie der AG Medizinische Bild- und Signalverarbeitung der GMDS und des Fachausschusses Biosignale der DGBMT werden wir neue Ansätze und Lösungen für Mess- und Analyseverfahren zu Blutdruck und -fluss sowie zum Gefäßzustand vorstellen und diskutieren. Dabei stehen im ersten Workshop auf der GMDS Jahrestagung Bildbasierte Verfahren im Zentrum, während der zweite Workshop auf der DGBMT Jahrestagung den Fokus auf Biosignalbasierten Verfahren legt. Es werden aktuelle Forschungsergebnisse vorgestellt und diskutiert. Es sind jeweils mehrere Vorträge geplant mit ausreichend Zeit zur Diskussion. Folgende Vorträge sind geplant (Arbeitstitel): Sebastian Zaunseder: Videobasierte Erfassung des Blutdrucks Anja Hennemuth: A Visualization Toolkit for the Analysis of Aortic Anatomy and Pressure Distribution Lars Krenkel: Numerische Analyse der Rupturwahrscheinlichkeit zerebraler Aneurysmata Susanne Schnell: Messung des Blutflusses und hämodynamischer Parameter mit 4D flow MRI: Möglichkeiten und Herausforderungen Ergebnisse: Ziel des Workshops ist die Identifikation von innovativen Ansätzen und neuen Methoden zur qualitativen und quantitativen Bestimmung von hämodynamischen Parametern sowie deren kritische Bewertung durch die Community für die Eignung in der klinischen Entscheidungsunterstützung. Diskussion: Der Workshop leistet inhaltlich einen Beitrag zu zentralen Aspekten für die Herz-Kreislauf-Medizin. Er bringt dabei Expertise aus verschiedenen Bereichen zusammen und schlägt die Brücke zwischen Kardiologie, Medizininformatik und Medizintechnik. Schlussfolgerung: Innovative Technologien aus Medizintechnik und Informatik ermöglichen zunehmend einfache und raumzeitlich aufgelöste Erfassung und Bewertung wichtiger Informationen zur Unterstützung von Diagnose und Therapieverfolgung. [1] Zaunseder S, Trumpp A, Wedekind D, Malberg H. Cardiovascular assessment by imaging photoplethysmography - a review. Biomed Tech (Berl). 2018 Oct 25;63(5):617–34. [2] Huellebrand M, Messroghli D, Tautz L, Kuehne T, Hennemuth A. An extensible software platform for interdisciplinary cardiovascular imaging research. Comput Methods Programs Biomed. 2020 Feb;184:105277. [3] Schmitter S, Adriany G, Waks M, Moeller S, Aristova M, Vali A, et al. Bilateral Multiband 4D Flow MRI of the Carotid Arteries at 7T. Magn Reson Med. 2020 Oct;84(4):1947–60. [4] Birkenmaier C, and Krenkel, L. Flow in Artificial Lungs. In: New Results in Numerical and Experimental Fluid Mechanics XIII. Contributions to the 22nd STAB/DGLR Symposium. Springer; 2021. KW - Bildbasierte Verfahren KW - Blutdruck KW - Hämodynamik KW - Blutgefäß KW - Bildgebendes Verfahren Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0183-21gmds0167 ER - TY - GEN A1 - Römmele, Christoph A1 - Mendel, Robert A1 - Rauber, David A1 - Rückert, Tobias A1 - Byrne, Michael F. A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Endoscopic Diagnosis of Eosinophilic Esophagitis Using a deep Learning Algorithm T2 - Endoscopy N2 - 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. KW - Eosinophilic Esophagitis KW - Endoscopy KW - Deep Learning Y1 - 2021 U6 - https://doi.org/10.1055/s-0041-1724274 VL - 53 IS - S 01 PB - Georg Thieme Verlag CY - Stuttgart ER - TY - GEN A1 - Meinikheim, Michael A1 - Mendel, Robert A1 - Probst, Andreas A1 - Scheppach, Markus W. A1 - Schnoy, Elisabeth A1 - Nagl, Sandra A1 - Römmele, Christoph A1 - Prinz, Friederike A1 - Schlottmann, Jakob A1 - Golger, Daniela A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - AI-assisted detection and characterization of early Barrett's neoplasia: Results of an Interim analysis T2 - Endoscopy N2 - Aims  Evaluation of the add-on effect an artificial intelligence (AI) based clinical decision support system has on the performance of endoscopists with different degrees of expertise in the field of Barrett's esophagus (BE) and Barrett's esophagus-related neoplasia (BERN). Methods  The support system is based on a multi-task deep learning model trained to solve a segmentation and several classification tasks. The training approach represents an extension of the ECMT semi-supervised learning algorithm. The complete system evaluates a decision tree between estimated motion, classification, segmentation, and temporal constraints, to decide when and how the prediction is highlighted to the observer. In our current study, ninety-six video cases of patients with BE and BERN were prospectively collected and assessed by Barrett's specialists and non-specialists. All video cases were evaluated twice – with and without AI assistance. The order of appearance, either with or without AI support, was assigned randomly. Participants were asked to detect and characterize regions of dysplasia or early neoplasia within the video sequences. Results  Standalone sensitivity, specificity, and accuracy of the AI system were 92.16%, 68.89%, and 81.25%, respectively. Mean sensitivity, specificity, and accuracy of expert endoscopists without AI support were 83,33%, 58,20%, and 71,48 %, respectively. Gastroenterologists without Barrett's expertise but with AI support had a comparable performance with a mean sensitivity, specificity, and accuracy of 76,63%, 65,35%, and 71,36%, respectively. Conclusions  Non-Barrett's experts with AI support had a similar performance as experts in a video-based study. Y1 - 2023 U6 - https://doi.org/10.1055/s-0043-1765437 VL - 55 IS - S02 PB - Thieme ER - TY - GEN A1 - Scheppach, Markus W. A1 - Rauber, David A1 - Mendel, Robert A1 - Palm, Christoph A1 - Byrne, Michael F. A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Detection Of Celiac Disease Using A Deep Learning Algorithm T2 - Endoscopy N2 - 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. KW - Celiac Disease KW - Deep Learning Y1 - 2021 U6 - https://doi.org/10.1055/s-0041-1724970 N1 - Digital poster exhibition VL - 53 IS - S 01 PB - Georg Thieme Verlag CY - Stuttgart ER -