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 - 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 - Scheppach, Markus W. A1 - Mendel, Robert A1 - Probst, Andreas A1 - Rauber, David A1 - Rueckert, Tobias A1 - Meinikheim, Michael A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Real-time detection and delineation of tissue during third-space endoscopy using artificial intelligence (AI) T2 - Endoscopy N2 - Aims  AI has proven great potential in assisting endoscopists in diagnostics, however its role in therapeutic endoscopy remains unclear. Endoscopic submucosal dissection (ESD) is a technically demanding intervention with a slow learning curve and relevant risks like bleeding and perforation. Therefore, we aimed to develop an algorithm for the real-time detection and delineation of relevant structures during third-space endoscopy. Methods  5470 still images from 59 full length videos (47 ESD, 12 POEM) were annotated. 179681 additional unlabeled images were added to the training dataset. Consequently, a DeepLabv3+ neural network architecture was trained with the ECMT semi-supervised algorithm (under review elsewhere). Evaluation of vessel detection was performed on a dataset of 101 standardized video clips from 15 separate third-space endoscopy videos with 200 predefined blood vessels. Results  Internal validation yielded an overall mean Dice score of 85% (68% for blood vessels, 86% for submucosal layer, 88% for muscle layer). On the video test data, the overall vessel detection rate (VDR) was 94% (96% for ESD, 74% for POEM). The median overall vessel detection time (VDT) was 0.32 sec (0.3 sec for ESD, 0.62 sec for POEM). Conclusions  Evaluation of the developed algorithm on a video test dataset showed high VDR and quick VDT, especially for ESD. Further research will focus on a possible clinical benefit of the AI application for VDR and VDT during third-space endoscopy. KW - Speiseröhrenkrankheit KW - Künstliche Intelligenz KW - Artificial Intelligence Y1 - 2023 U6 - https://doi.org/10.1055/s-0043-1765128 VL - 55 IS - S02 SP - S53 EP - S54 PB - Thieme ER - TY - GEN A1 - Scheppach, Markus W. A1 - Mendel, Robert A1 - Probst, Andreas A1 - Nagl, Sandra A1 - Meinikheim, Michael A1 - Yip, Hon Chi A1 - Lau, Louis Ho Shing A1 - Chiu, Philip Wai Yan A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Effekt eines Künstliche Intelligenz (KI) – Algorithmus auf die Gefäßdetektion bei third space Endoskopien T2 - Zeitschrift für Gastroenterologie N2 - Einleitung  Third space Endoskopieprozeduren wie die endoskopische Submukosadissektion (ESD) und die perorale endoskopische Myotomie (POEM) sind technisch anspruchsvoll und gehen mit untersucherabhängigen Komplikationen wie Blutungen und Perforationen einher. Grund hierfür ist die unabsichtliche Durchschneidung von submukosalen Blutgefäßen ohne präemptive Koagulation. Ziele Die Forschungsfrage, ob ein KI-Algorithmus die intraprozedurale Gefäßerkennung bei ESD und POEM unterstützen und damit Komplikationen wie Blutungen verhindern könnte, erscheint in Anbetracht des erfolgreichen Einsatzes von KI bei der Erkennung von Kolonpolypen interessant. Methoden  Auf 5470 Einzelbildern von 59 third space Endoscopievideos wurden submukosale Blutgefäße annotiert. Zusammen mit weiteren 179.681 nicht-annotierten Bildern wurde ein DeepLabv3+neuronales Netzwerk mit dem ECMT-Verfahren für semi-supervised learning trainiert, um Blutgefäße in Echtzeit erkennen zu können. Für die Evaluation wurde ein Videotest mit 101 Videoclips aus 15 vom Trainingsdatensatz separaten Prozeduren mit 200 vordefinierten Gefäßen erstellt. Die Gefäßdetektionsrate, -zeit und -dauer, definiert als der Prozentsatz an Einzelbildern eines Videos bezogen auf den Goldstandard, auf denen ein definiertes Gefäß erkannt wurde, wurden erhoben. Acht erfahrene Endoskopiker wurden mithilfe dieses Videotests im Hinblick auf Gefäßdetektion getestet, wobei eine Hälfte der Videos nativ, die andere Hälfte nach Markierung durch den KI-Algorithmus angesehen wurde. Ergebnisse  Der mittlere Dice Score des Algorithmus für Blutgefäße war 68%. Die mittlere Gefäßdetektionsrate im Videotest lag bei 94% (96% für ESD; 74% für POEM). Die mediane Gefäßdetektionszeit des Algorithmus lag bei 0,32 Sekunden (0,3 Sekunden für ESD; 0,62 Sekunden für POEM). Die mittlere Gefäßdetektionsdauer lag bei 59,1% (60,6% für ESD; 44,8% für POEM) des Goldstandards. Alle Endoskopiker hatten mit KI-Unterstützung eine höhere Gefäßdetektionsrate als ohne KI. Die mittlere Gefäßdetektionsrate ohne KI lag bei 56,4%, mit KI bei 71,2% (p<0.001). Schlussfolgerung  KI-Unterstützung war mit einer statistisch signifikant höheren Gefäßdetektionsrate vergesellschaftet. Die mediane Gefäßdetektionszeit von deutlich unter einer Sekunde sowie eine Gefäßdetektionsdauer von größer 50% des Goldstandards wurden für den klinischen Einsatz als ausreichend erachtet. In prospektiven Anwendungsstudien sollte der KI-Algorithmus auf klinische Relevanz getestet werden. KW - Künstliche Intelligenz Y1 - 2023 U6 - https://doi.org/10.1055/s-0043-1771980 VL - 61 IS - 08 PB - Thieme CY - Stuttgart ER - TY - GEN A1 - Scheppach, Markus W. A1 - Mendel, Robert A1 - Probst, Andreas A1 - Meinikheim, Michael A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Artificial Intelligence (AI) – assisted vessel and tissue recognition during third space endoscopy (Smart ESD) T2 - Zeitschrift für Gastroenterologie N2 - 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. KW - Artificial Intelligence KW - Medical Image Computing KW - Endoscopy KW - Bildgebendes Verfahren KW - Medizin KW - Künstliche Intelligenz KW - Endoskopie Y1 - 2022 U6 - https://doi.org/10.1055/s-0042-1755110 VL - 60 IS - 08 PB - Georg Thieme Verlag CY - Stuttgart ER - TY - GEN A1 - Scheppach, Markus W. A1 - Mendel, Robert A1 - Probst, Andreas A1 - Meinikheim, Michael A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Intraprozedurale Strukturerkennung bei Third-Space Endoskopie mithilfe eines Deep-Learning Algorithmus T2 - Zeitschrift für Gastroenterologie N2 - Einleitung Third-Space Interventionen wie die endoskopische Submukosadissektion (ESD) und die perorale endoskopische Myotomie (POEM) sind technisch anspruchsvoll und mit einem erhöhten Risiko für intraprozedurale Komplikationen wie Blutung oder Perforation assoziiert. Moderne Computerprogramme zur Unterstützung bei diagnostischen Entscheidungen werden unter Einsatz von künstlicher Intelligenz (KI) in der Endoskopie bereits erfolgreich eingesetzt. Ziel der vorliegenden Arbeit war es, relevante anatomische Strukturen mithilfe eines Deep-Learning Algorithmus zu detektieren und segmentieren, um die Sicherheit und Anwendbarkeit von ESD und POEM zu erhöhen. Methoden Zwölf Videoaufnahmen in voller Länge von Third-Space Endoskopien wurden aus der Datenbank des Universitätsklinikums Augsburg extrahiert. 1686 Einzelbilder wurden für die Kategorien Submukosa, Blutgefäß, Dissektionsmesser und endoskopisches Instrument annotiert und segmentiert. Mit diesem Datensatz wurde ein DeepLabv3+neuronales Netzwerk auf der Basis eines ResNet mit 101 Schichten trainiert und intern anhand der Parameter Intersection over Union (IoU), Dice Score und Pixel Accuracy validiert. Die Fähigkeit des Algorithmus zur Gefäßdetektion wurde anhand von 24 Videoclips mit einer Spieldauer von 7 bis 46 Sekunden mit 33 vordefinierten Gefäßen evaluiert. Anhand dieses Tests wurde auch die Gefäßdetektionsrate eines Experten in der Third-Space Endoskopie ermittelt. Ergebnisse Der Algorithmus zeigte eine Gefäßdetektionsrate von 93,94% mit einer mittleren Rate an falsch positiven Signalen von 1,87 pro Minute. Die Gefäßdetektionsrate des Experten lag bei 90,1% ohne falsch positive Ergebnisse. In der internen Validierung an Einzelbildern wurde eine IoU von 63,47%, ein mittlerer Dice Score von 76,18% und eine Pixel Accuracy von 86,61% ermittelt. Zusammenfassung Dies ist der erste KI-Algorithmus, der für den Einsatz in der therapeutischen Endoskopie entwickelt wurde. Präliminäre Ergebnisse deuten auf eine mit Experten vergleichbare Detektion von Gefäßen während der Untersuchung hin. Weitere Untersuchungen sind nötig, um die Leistung des Algorithmus im Vergleich zum Experten genauer zu eruieren sowie einen möglichen klinischen Nutzen zu ermitteln. KW - Deep Learning KW - Third-Space Endoscopy Y1 - 2022 U6 - https://doi.org/10.1055/s-0042-1745652 VL - 60 IS - 04 PB - Thieme CY - Stuttgart 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 - Roser, D. A. A1 - Meinikheim, Michael A1 - Mendel, Robert A1 - Palm, Christoph A1 - Probst, Andreas A1 - Muzalyova, A. A1 - Scheppach, Markus W. A1 - Nagl, S. A1 - Schnoy, Elisabeth A1 - Römmele, Christoph A1 - Schulz, D. A1 - Schlottmann, Jakob A1 - Prinz, Friederike A1 - Rauber, David A1 - Rückert, Tobias A1 - Matsumura, T. A1 - Fernandez-Esparrach, G. A1 - Parsa, N. A1 - Byrne, M. 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 - Mendel, Robert A1 - De Souza Jr., Luis Antonio A1 - Rauber, David A1 - Papa, João Paulo A1 - Palm, Christoph T1 - Abstract: Semi-supervised Segmentation Based on Error-correcting Supervision T2 - Bildverarbeitung für die Medizin 2021. Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9, 2021 N2 - Pixel-level classification is an essential part of computer vision. For learning from labeled data, many powerful deep learning models have been developed recently. In this work, we augment such supervised segmentation models by allowing them to learn from unlabeled data. Our semi-supervised approach, termed Error-Correcting Supervision, leverages a collaborative strategy. Apart from the supervised training on the labeled data, the segmentation network is judged by an additional network. KW - Deep Learning Y1 - 2021 SN - 978-3-658-33197-9 U6 - https://doi.org/10.1007/978-3-658-33198-6_43 SP - 178 PB - Springer Vieweg CY - Wiesbaden ER - TY - GEN A1 - Meinikheim, Michael A1 - Mendel, Robert A1 - Scheppach, Markus W. A1 - Probst, Andreas A1 - Prinz, Friederike A1 - Schwamberger, Tanja A1 - Schlottmann, Jakob A1 - Gölder, Stefan Karl A1 - Walter, Benjamin A1 - Steinbrück, Ingo A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - INFLUENCE OF AN ARTIFICIAL INTELLIGENCE (AI) BASED DECISION SUPPORT SYSTEM (DSS) ON THE DIAGNOSTIC PERFORMANCE OF NON-EXPERTS IN BARRETT´S ESOPHAGUS RELATED NEOPLASIA (BERN) T2 - Endoscopy N2 - Aims Barrett´s esophagus related neoplasia (BERN) is difficult to detect and characterize during endoscopy, even for expert endoscopists. We aimed to assess the add-on effect of an Artificial Intelligence (AI) algorithm (Barrett-Ampel) as a decision support system (DSS) for non-expert endoscopists in the evaluation of Barrett’s esophagus (BE) and BERN. Methods Twelve videos with multimodal imaging white light (WL), narrow-band imaging (NBI), texture and color enhanced imaging (TXI) of histologically confirmed BE and BERN were assessed by expert and non-expert endoscopists. For each video, endoscopists were asked to identify the area of BERN and decide on the biopsy spot. Videos were assessed by the AI algorithm and regions of BERN were highlighted in real-time by a transparent overlay. Finally, endoscopists were shown the AI videos and asked to either confirm or change their initial decision based on the AI support. Results Barrett-Ampel correctly identified all areas of BERN, irrespective of the imaging modality (WL, NBI, TXI), but misinterpreted two inflammatory lesions (Accuracy=75%). Expert endoscopists had a similar performance (Accuracy=70,8%), while non-experts had an accuracy of 58.3%. When AI was implemented as a DSS, non-expert endoscopists improved their diagnostic accuracy to 75%. Conclusions AI may have the potential to support non-expert endoscopists in the assessment of videos of BE and BERN. Limitations of this study include the low number of videos used. Randomized clinical trials in a real-life setting should be performed to confirm these results. KW - Artificial Intelligence KW - Barrett's Esophagus KW - Speiseröhrenkrankheit KW - Künstliche Intelligenz KW - Diagnose Y1 - 2022 U6 - https://doi.org/10.1055/s-00000012 VL - 54 IS - S 01 SP - S39 PB - Thieme ER -