TY - JOUR 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 - Stallmach, Andreas A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Detection of duodenal villous atrophy on endoscopic images using a deep learning algorithm JF - Gastrointestinal Endoscopy N2 - Background and aims Celiac disease with its endoscopic manifestation of villous atrophy is underdiagnosed worldwide. The application of artificial intelligence (AI) for the macroscopic detection of villous atrophy at routine esophagogastroduodenoscopy may improve diagnostic performance. Methods A dataset of 858 endoscopic images of 182 patients with villous atrophy and 846 images from 323 patients with normal duodenal mucosa was collected and used to train a ResNet 18 deep learning model to detect villous atrophy. An external data set was used to test the algorithm, in addition to six fellows and four board certified gastroenterologists. Fellows could consult the AI algorithm’s result during the test. From their consultation distribution, a stratification of test images into “easy” and “difficult” was performed and used for classified performance measurement. Results External validation of the AI algorithm yielded values of 90 %, 76 %, and 84 % for sensitivity, specificity, and accuracy, respectively. Fellows scored values of 63 %, 72 % and 67 %, while the corresponding values in experts were 72 %, 69 % and 71 %, respectively. AI consultation significantly improved all trainee performance statistics. While fellows and experts showed significantly lower performance for “difficult” images, the performance of the AI algorithm was stable. Conclusion In this study, an AI algorithm outperformed endoscopy fellows and experts in the detection of villous atrophy on endoscopic still images. AI decision support significantly improved the performance of non-expert endoscopists. The stable performance on “difficult” images suggests a further positive add-on effect in challenging cases. KW - celiac disease KW - villous atrophy KW - endoscopy detection KW - artificial intelligence Y1 - 2023 U6 - https://doi.org/10.1016/j.gie.2023.01.006 PB - Elsevier ER - TY - GEN A1 - Scheppach, Markus 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 - 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 - 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 - 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 - TY - JOUR A1 - Meinikheim, Michael A1 - Mendel, Robert A1 - Palm, Christoph A1 - Probst, Andreas A1 - Muzalyova, Anna A1 - Scheppach, Markus Wolfgang 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 - Fernández-Esparrach, Glòria A1 - Parsa, Nasim A1 - Byrne, Michael F A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Effect of AI on performance of endoscopists to detect Barrett neoplasia: A Randomized Tandem Trial JF - Endoscopy N2 - Background and study aims To evaluate the effect of an AI-based clinical decision support system (AI) on the performance and diagnostic confidence of endoscopists during the assessment of Barrett's esophagus (BE). Patients and Methods Ninety-six standardized endoscopy videos were assessed by 22 endoscopists from 12 different centers with varying degrees of BE experience. The assessment was randomized into two video sets: Group A (review first without AI and second with AI) and group B (review first with AI and second without AI). Endoscopists were required to evaluate each video for the presence of Barrett's esophagus-related neoplasia (BERN) and then decide on a spot for a targeted biopsy. After the second assessment, they were allowed to change their clinical decision and confidence level. Results AI had a standalone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.6%, respectively. Without AI, BE experts had an overall sensitivity, specificity, and accuracy of 83.3%, 58.1 and 71.5%, respectively. With AI, BE nonexperts showed a significant improvement in sensitivity and specificity when videos were assessed a second time with AI (sensitivity 69.7% (95% CI, 65.2% - 74.2%) to 78.0% (95% CI, 74.0% - 82.0%); specificity 67.3% (95% CI, 62.5% - 72.2%) to 72.7% (95 CI, 68.2% - 77.3%). In addition, the diagnostic confidence of BE nonexperts improved significantly with AI. Conclusion BE nonexperts benefitted significantly from the additional AI. BE experts and nonexperts remained below the standalone performance of AI, suggesting that there may be other factors influencing endoscopists to follow or discard AI advice. Y1 - 2024 U6 - https://doi.org/10.1055/a-2296-5696 SN - 0013-726X N1 - Accepted Manuscript PB - Georg Thieme Verlag ER - TY - JOUR 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 IN THIRD-SPACE ENDOSCOPY JF - Endoscopy N2 - Aims Third-space endoscopy procedures such as endoscopic submucosal dissection (ESD) and peroral endoscopic myotomy (POEM) are complex interventions with elevated risk of operator-dependent adverse events, such as intra-procedural bleeding and perforation. We aimed to design an artificial intelligence clinical decision support solution (AI-CDSS, “Smart ESD”) for the detection and delineation of vessels, tissue structures, and instruments during third-space endoscopy procedures. Methods Twelve full-length third-space endoscopy videos were extracted from the Augsburg University Hospital database. 1686 frames were annotated for the following categories: Submucosal layer, blood vessels, electrosurgical knife and endoscopic instrument. A DeepLabv3+neural network with a 101-layer ResNet backbone was trained and validated internally. Finally, the ability of the AI system to detect visible vessels during ESD and POEM was determined on 24 separate video clips of 7 to 46 seconds duration and showing 33 predefined vessels. These video clips were also assessed by an expert in third-space endoscopy. Results Smart ESD showed a vessel detection rate (VDR) of 93.94%, while an average of 1.87 false positive signals were recorded per minute. VDR of the expert endoscopist was 90.1% with no false positive findings. On the internal validation data set using still images, the AI system demonstrated an Intersection over Union (IoU), mean Dice score and pixel accuracy of 63.47%, 76.18% and 86.61%, respectively. Conclusions This is the first AI-CDSS aiming to mitigate operator-dependent limitations during third-space endoscopy. Further clinical trials are underway to better understand the role of AI in such procedures. KW - Artificial Intelligence KW - Third-Space Endoscopy KW - Smart ESD Y1 - 2022 U6 - https://doi.org/10.1055/s-0042-1745037 VL - 54 IS - S01 SP - S175 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 - TY - CHAP A1 - Rauber, David A1 - Mendel, Robert A1 - Scheppach, Markus W. A1 - Ebigbo, Alanna A1 - Messmann, Helmut A1 - Palm, Christoph T1 - Analysis of Celiac Disease with Multimodal Deep Learning T2 - Bildverarbeitung für die Medizin 2022: Proceedings, German Workshop on Medical Image Computing, Heidelberg, June 26-28, 2022 N2 - Celiac disease is an autoimmune disorder caused by gluten that results in an inflammatory response of the small intestine.We investigated whether celiac disease can be detected using endoscopic images through a deep learning approach. The results show that additional clinical parameters can improve the classification accuracy. In this work, we distinguished between healthy tissue and Marsh III, according to the Marsh score system. We first trained a baseline network to classify endoscopic images of the small bowel into these two classes and then augmented the approach with a multimodality component that took the antibody status into account. KW - Deep Learning KW - Endoscopy Y1 - 2022 U6 - https://doi.org/10.1007/978-3-658-36932-3_25 SP - 115 EP - 120 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 - Einsatz von künstlicher Intelligenz (KI) als Entscheidungsunterstützungssystem für nicht-Experten bei der Beurteilung von Barrett-Ösophagus assoziierten Neoplasien (BERN) T2 - Zeitschrift für Gastroenterologie N2 - Einleitung Die sichere Detektion und Charakterisierung von Barrett-Ösophagus assoziierten Neoplasien (BERN) stellt selbst für erfahrene Endoskopiker eine Herausforderung dar. Ziel Ziel dieser Studie ist es, den Add-on Effekt eines künstlichen Intelligenz (KI) Systems (Barrett-Ampel) als Entscheidungsunterstüzungssystem für Endoskopiker ohne Expertise bei der Untersuchung von BERN zu evaluieren. Material und Methodik Zwölf Videos in „Weißlicht“ (WL), „narrow-band imaging“ (NBI) und „texture and color enhanced imaging“ (TXI) von histologisch bestätigten Barrett-Metaplasien oder BERN wurden von Experten und Untersuchern ohne Barrett-Expertise evaluiert. Die Probanden wurden dazu aufgefordert in den Videos auftauchende BERN zu identifizieren und gegebenenfalls die optimale Biopsiestelle zu markieren. Unser KI-System wurde demselben Test unterzogen, wobei dieses BERN in Echtzeit segmentierte und farblich von umliegendem Epithel differenzierte. Anschließend wurden den Probanden die Videos mit zusätzlicher KI-Unterstützung gezeigt. Basierend auf dieser neuen Information, wurden die Probanden zu einer Reevaluation ihrer initialen Beurteilung aufgefordert. Ergebnisse Die „Barrett-Ampel“ identifizierte unabhängig von den verwendeten Darstellungsmodi (WL, NBI, TXI) alle BERN. Zwei entzündlich veränderte Läsionen wurden fehlinterpretiert (Genauigkeit=75%). Während Experten vergleichbare Ergebnisse erzielten (Genauigkeit=70,8%), hatten Endoskopiker ohne Expertise bei der Beurteilung von Barrett-Metaplasien eine Genauigkeit von lediglich 58,3%. Wurden die nicht-Experten allerdings von unserem KI-System unterstützt, erreichten diese eine Genauigkeit von 75%. Zusammenfassung Unser KI-System hat das Potential als Entscheidungsunterstützungssystem bei der Differenzierung zwischen Barrett-Metaplasie und BERN zu fungieren und so Endoskopiker ohne entsprechende Expertise zu assistieren. Eine Limitation dieser Studie ist die niedrige Anzahl an eingeschlossenen Videos. Um die Ergebnisse dieser Studie zu bestätigen, müssen randomisierte kontrollierte klinische Studien durchgeführt werden. KW - Barrett-Ösophagus KW - Künstliche Intelligenz Y1 - 2022 U6 - https://doi.org/10.1055/s-0042-1745653 VL - 60 IS - 4 SP - 251 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 - 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 - Meinikheim, Michael A1 - Mendel, Robert A1 - Probst, Andreas A1 - Scheppach, Markus W. A1 - Nagl, Sandra A1 - Schnoy, Elisabeth A1 - Römmele, Christoph A1 - Prinz, Friederike A1 - Schlottmann, Jakob A1 - Messmann, Helmut A1 - Palm, Christoph A1 - Ebigbo, Alanna T1 - Einfluss von Künstlicher Intelligenz auf die Performance von niedergelassenen Gastroenterolog:innen bei der Beurteilung von Barrett-Ösophagus T2 - Zeitschrift für Gastroenterologie N2 - Einleitung  Die Differenzierung zwischen nicht dysplastischem Barrett-Ösophagus (NDBE) und mit Barrett-Ösophagus assoziierten Neoplasien (BERN) während der endoskopischen Inspektion erfordert viel Expertise. Die frühe Diagnosestellung ist wichtig für die weitere Prognose des Barrett-Karzinoms. In Deutschland werden Patient:innen mit einem Barrett-Ösophagus (BE) in der Regel im niedergelassenen Sektor überwacht. Ziele  Ziel ist es, den Einfluss von einem auf Künstlicher Intelligenz (KI) basierenden klinischen Entscheidungsunterstützungssystems (CDSS) auf die Performance von niedergelassenen Gastroenterolog:innen (NG) bei der Evaluation von Barrett-Ösophagus (BE) zu untersuchen. Methodik  Es erfolgte die prospektive Sammlung von 96 unveränderten hochauflösenden Videos mit Fällen von Patient:innen mit histologisch bestätigtem NDBE und BERN. Alle eingeschlossenen Fälle enthielten mindestens zwei der folgenden Darstellungsmethoden: HD-Weißlichtendoskopie, Narrow Band Imaging oder Texture and Color Enhancement Imaging. Sechs NG von sechs unterschiedlichen Praxen wurden als Proband:innen eingeschlossen. Es erfolgte eine permutierte Block-Randomisierung der Videofälle in entweder Gruppe A oder Gruppe B. Gruppe A implizierte eine Evaluation des Falls durch Proband:innen zunächst ohne KI und anschließend mit KI als CDSS. In Gruppe B erfolgte die Evaluation in umgekehrter Reihenfolge. Anschließend erfolgte eine zufällige Wiedergabe der so entstandenen Subgruppen im Rahmen des Tests. Ergebnis  In diesem Test konnte ein von uns entwickeltes KI-System (Barrett-Ampel) eine Sensitivität von 92,2%, eine Spezifität von 68,9% und eine Accuracy von 81,3% erreichen. Mit der Hilfe von KI verbesserte sich die Sensitivität der NG von 64,1% auf 71,2% (p<0,001) und die Accuracy von 66,3% auf 70,8% (p=0,006) signifikant. Eine signifikante Verbesserung dieser Parameter zeigte sich ebenfalls, wenn die Proband:innen die Fälle zunächst ohne KI evaluierten (Gruppe A). Wurde der Fall jedoch als Erstes mit der Hilfe von KI evaluiert (Gruppe B), blieb die Performance nahezu konstant. Schlussfolgerung  Es konnte ein performantes KI-System zur Evaluation von BE entwickelt werden. NG verbessern sich bei der Evaluation von BE durch den Einsatz von KI. KW - Barrett-Ösophagus KW - Künstliche Intelligenz Y1 - 2023 UR - https://www.thieme-connect.de/products/ejournals/abstract/10.1055/s-0043-1771711 U6 - https://doi.org/10.1055/s-0043-1771711 VL - 61 IS - 8 PB - Thieme CY - Stuttgart 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 - Meinikheim, Michael A1 - Mendel, Robert A1 - Probst, Andreas A1 - Scheppach, Markus W. A1 - Messmann, Helmut A1 - Palm, Christoph A1 - Ebigbo, Alanna T1 - Barrett-Ampel T2 - Zeitschrift für Gastroenterologie N2 - Hintergrund  Adenokarzinome des Ösophagus sind bis heute mit einer infausten Prognose vergesellschaftet (1). Obwohl Endoskopiker mit Barrett-Ösophagus als Präkanzerose konfrontiert werden, ist vor allem für nicht-Experten die Differenzierung zwischen Barrett-Ösophagus ohne Dysplasie und assoziierten Neoplasien mitunter schwierig. Existierende Biopsieprotokolle (z.B. Seattle Protokoll) sind oftmals unzuverlässig (2). Eine frühzeitige Diagnose des Adenokarzinoms ist allerdings von fundamentaler Bedeutung für die Prognose des Patienten. Forschungsansatz  Auf der Grundlage dieser Problematik, entwickelten wir in Kooperation mit dem Forschungslabor „Regensburg Medical Image Computing (ReMIC)“ der OTH Regensburg ein auf künstlicher Intelligenz (KI) basiertes Entscheidungsunterstützungssystem (CDSS). Das auf einer DeepLabv3+ neuronalen Netzwerkarchitektur basierende CDSS differenziert mittels Mustererkennung Barrett- Ösophagus ohne Dysplasie von Barrett-Ösophagus mit Dysplasie bzw. Neoplasie („Klassifizierung“). Hierbei werden gemittelte Ausgabewahrscheinlichkeiten mit einem vom Benutzer definierten Schwellenwert verglichen. Für Vorhersagen, die den Schwellenwert überschreiten, berechnen wir die Kontur der Region und die Fläche. Sobald die vorhergesagte Läsion eine bestimmte Größe in der Eingabe überschreitet, heben wir sie und ihren Umriss hervor. So ermöglicht eine farbkodierte Visualisierung eine Abgrenzung zwischen Dysplasie bzw. Neoplasie und normalem Barrett-Epithel („Segmentierung“). In einer Studie an Bildern in „Weißlicht“ (WL) und „Narrow Band Imaging“ (NBI) demonstrierten wir eine Sensitivität von mehr als 90% und eine Spezifität von mehr als 80% (3). In einem nächsten Schritt, differenzierte unser KI-Algorithmus Barrett- Metaplasien von assoziierten Neoplasien anhand von zufällig abgegriffenen Bildern in Echtzeit mit einer Accuracy von 89.9% (4). Darauf folgend, entwickelten wir unser System dahingehend weiter, dass unser Algorithmus nun auch dazu in der Lage ist, Untersuchungsvideos in WL, NBI und „Texture and Color Enhancement Imaging“ (TXI) in Echtzeit zu analysieren (5). Aktuell führen wir eine Studie in einem randomisiert-kontrollierten Ansatz an unveränderten Untersuchungsvideos in WL, NBI und TXI durch. Ausblick  Um Patienten mit aus Barrett-Metaplasien resultierenden Neoplasien frühestmöglich an „High-Volume“-Zentren überweisen zu können, soll unser KI-Algorithmus zukünftig vor allem Endoskopiker ohne extensive Erfahrung bei der Beurteilung von Barrett- Ösophagus in der Krebsfrüherkennung unterstützen. KW - Barrett-Ösophagus KW - Adenokarzinom KW - Künstliche Intelligenz KW - Speiseröhrenkrebs KW - Diagnose KW - Künstliche Intelligenz Y1 - 2022 U6 - https://doi.org/10.1055/s-0042-1755109 VL - 60 IS - 08 PB - Georg Thieme Verlag CY - Stuttgart ER - TY - GEN A1 - Meinikheim, Michael A1 - Mendel, Robert A1 - Probst, Andreas A1 - Scheppach, Markus W. A1 - Messmann, Helmut A1 - Palm, Christoph A1 - Ebigbo, Alanna T1 - Optical Flow als Methode zur Qualitätssicherung KI-unterstützter Untersuchungen von Barrett-Ösophagus und Barrett-Ösophagus assoziierten Neoplasien T2 - Zeitschrift für Gastroenterologie N2 - Einleitung  Übermäßige Bewegung im Bild kann die Performance von auf künstlicher Intelligenz (KI) basierenden klinischen Entscheidungsunterstützungssystemen (CDSS) reduzieren. Optical Flow (OF) ist eine Methode zur Lokalisierung und Quantifizierung von Bewegungen zwischen aufeinanderfolgenden Bildern. Ziel  Ziel ist es, die Mensch-Computer-Interaktion (HCI) zu verbessern und Endoskopiker die unser KI-System „Barrett-Ampel“ zur Unterstützung bei der Beurteilung von Barrett-Ösophagus (BE) verwenden, ein Echtzeit-Feedback zur aktuellen Datenqualität anzubieten. Methodik  Dazu wurden unveränderte Videos in „Weißlicht“ (WL), „Narrow Band Imaging“ (NBI) und „Texture and Color Enhancement Imaging“ (TXI) von acht endoskopischen Untersuchungen von histologisch gesichertem BE und mit Barrett-Ösophagus assoziierten Neoplasien (BERN) durch unseren KI-Algorithmus analysiert. Der zur Bewertung der Bildqualität verwendete OF beinhaltete die mittlere Magnitude und die Entropie des Histogramms der Winkel. Frames wurden automatisch extrahiert, wenn die vordefinierten Schwellenwerte von 3,0 für die mittlere Magnitude und 9,0 für die Entropie des Histogramms der Winkel überschritten wurden. Experten sahen sich zunächst die Videos ohne KI-Unterstützung an und bewerteten, ob Störfaktoren die Sicherheit mit der eine Diagnose im vorliegenden Fall gestellt werden kann negativ beeinflussen. Anschließend überprüften sie die extrahierten Frames. Ergebnis  Gleichmäßige Bewegung in eine Richtung, wie etwa beim Vorschieben des Endoskops, spiegelte sich, bei insignifikant veränderter Entropie, in einer Erhöhung der Magnitude wider. Chaotische Bewegung, zum Beispiel während dem Spülen, war mit erhöhter Entropie assoziiert. Insgesamt war eine unruhige endoskopische Darstellung, Flüssigkeit sowie übermäßige Ösophagusmotilität mit erhöhtem OF assoziiert und korrelierte mit der Meinung der Experten über die Qualität der Videos. Der OF und die subjektive Wahrnehmung der Experten über die Verwertbarkeit der vorliegenden Bildsequenzen korrelierten direkt proportional. Wenn die vordefinierten Schwellenwerte des OF überschritten wurden, war die damit verbundene Bildqualität in 94% der Fälle für eine definitive Interpretation auch für Experten unzureichend. Schlussfolgerung  OF hat das Potenzial Endoskopiker ein Echtzeit-Feedback über die Qualität des Dateninputs zu bieten und so nicht nur die HCI zu verbessern, sondern auch die optimale Performance von KI-Algorithmen zu ermöglichen. KW - Optical Flow Y1 - 2022 U6 - https://doi.org/10.1055/s-0042-1754997 VL - 60 IS - 08 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 - 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 - JOUR 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 A. H. A1 - Schlottmann, Jakob A1 - Prinz, Friederike A1 - Rauber, David A1 - Rueckert, Tobias A1 - Matsumura, Tomoaki A1 - Fernández-Esparrach, Glòria A1 - Parsa, Nasim A1 - Byrne, Michael F. A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett’s esophagus: a tandem randomized and video trial JF - Endoscopy N2 - Background This study evaluated the effect of an artificial intelligence (AI)-based clinical decision support system on the performance and diagnostic confidence of endoscopists in their assessment of Barrett’s esophagus (BE). Methods 96 standardized endoscopy videos were assessed by 22 endoscopists with varying degrees of BE experience from 12 centers. Assessment was randomized into two video sets: group A (review first without AI and second with AI) and group B (review first with AI and second without AI). Endoscopists were required to evaluate each video for the presence of Barrett’s esophagus-related neoplasia (BERN) and then decide on a spot for a targeted biopsy. After the second assessment, they were allowed to change their clinical decision and confidence level. Results AI had a stand-alone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.3%, respectively. Without AI, BE experts had an overall sensitivity, specificity, and accuracy of 83.3%, 58.1%, and 71.5%, respectively. With AI, BE nonexperts showed a significant improvement in sensitivity and specificity when videos were assessed a second time with AI (sensitivity 69.8% [95%CI 65.2%–74.2%] to 78.0% [95%CI 74.0%–82.0%]; specificity 67.3% [95%CI 62.5%–72.2%] to 72.7% [95%CI 68.2%–77.3%]). In addition, the diagnostic confidence of BE nonexperts improved significantly with AI. Conclusion BE nonexperts benefitted significantly from additional AI. BE experts and nonexperts remained significantly below the stand-alone performance of AI, suggesting that there may be other factors influencing endoscopists’ decisions to follow or discard AI advice. KW - Artificial Intelligence KW - Endoscopy KW - Medical Image Computing Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-72818 PB - Georg Thieme Verlag CY - Stuttgart 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 - 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 - 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 - JOUR A1 - Ebigbo, Alanna A1 - Mendel, Robert A1 - Scheppach, Markus W. A1 - Probst, Andreas A1 - Shahidi, Neal A1 - Prinz, Friederike A1 - Fleischmann, Carola A1 - Römmele, Christoph A1 - Gölder, Stefan Karl A1 - Braun, Georg A1 - Rauber, David A1 - Rückert, Tobias A1 - Souza Jr., Luis Antonio de A1 - Papa, João Paulo A1 - Byrne, Michael F. A1 - Palm, Christoph A1 - Messmann, Helmut T1 - Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm JF - Gut N2 - In this study, we aimed to develop an artificial intelligence clinical decision support solution to mitigate operator-dependent limitations during complex endoscopic procedures such as endoscopic submucosal dissection and peroral endoscopic myotomy, for example, bleeding and perforation. A DeepLabv3-based model was trained to delineate vessels, tissue structures and instruments on endoscopic still images from such procedures. The mean cross-validated Intersection over Union and Dice Score were 63% and 76%, respectively. Applied to standardised video clips from third-space endoscopic procedures, the algorithm showed a mean vessel detection rate of 85% with a false-positive rate of 0.75/min. These performance statistics suggest a potential clinical benefit for procedure safety, time and also training. KW - Artificial Intelligence KW - Endoscopy KW - Medical Image Computing Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-54293 VL - 71 IS - 12 SP - 2388 EP - 2390 PB - BMJ CY - London ER -