@article{MeinikheimMendelPalmetal., author = {Meinikheim, Michael and Mendel, Robert and Palm, Christoph and Probst, Andreas and Muzalyova, Anna and Scheppach, Markus W. and Nagl, Sandra and Schnoy, Elisabeth and R{\"o}mmele, Christoph and Schulz, Dominik Andreas Helmut Otto and Schlottmann, Jakob and Prinz, Friederike and Rauber, David and R{\"u}ckert, Tobias and Matsumura, Tomoaki and Fern{\´a}ndez-Esparrach, Gl{\`o}ria and Parsa, Nasim and Byrne, Michael F. and Messmann, Helmut and Ebigbo, Alanna}, title = {Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett's esophagus: a tandem randomized and video trial}, series = {Endoscopy}, volume = {56}, journal = {Endoscopy}, publisher = {Georg Thieme Verlag}, address = {Stuttgart}, doi = {10.1055/a-2296-5696}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-72818}, pages = {641 -- 649}, abstract = {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.}, language = {en} } @misc{ZellmerRauberProbstetal., author = {Zellmer, Stephan and Rauber, David and Probst, Andreas and Weber, Tobias and Braun, Georg and R{\"o}mmele, Christoph and Nagl, Sandra and Schnoy, Elisabeth and Messmann, Helmut and Ebigbo, Alanna and Palm, Christoph}, title = {Artificial intelligence as a tool in the detection of the papillary ostium during ERCP}, series = {Endoscopy}, volume = {56}, journal = {Endoscopy}, number = {S 02}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/s-0044-1783138}, pages = {S198}, abstract = {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.}, language = {en} } @misc{ScheppachRauberStallhoferetal., author = {Scheppach, Markus W. and Rauber, David and Stallhofer, Johannes and Muzalyova, Anna and Otten, Vera and Manzeneder, Carolin and Schwamberger, Tanja and Wanzl, Julia and Schlottmann, Jakob and Tadic, Vidan and Probst, Andreas and Schnoy, Elisabeth and R{\"o}mmele, Christoph and Fleischmann, Carola and Meinikheim, Michael and Miller, Silvia and M{\"a}rkl, Bruno and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {Performance comparison of a deep learning algorithm with endoscopists in the detection of duodenal villous atrophy (VA)}, series = {Endoscopy}, volume = {55}, journal = {Endoscopy}, number = {S02}, publisher = {Thieme}, doi = {10.1055/s-0043-1765421}, pages = {S165}, abstract = {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.}, language = {en} } @misc{RoserMeinikheimMendeletal., author = {Roser, David and Meinikheim, Michael and Mendel, Robert and Palm, Christoph and Probst, Andreas and Muzalyova, Anna and Scheppach, Markus W. and Nagl, Sandra and Schnoy, Elisabeth and R{\"o}mmele, Christoph and Schulz, Dominik Andreas Helmut Otto and Schlottmann, Jakob and Prinz, Friederike and Rauber, David and R{\"u}ckert, Tobias and Matsumura, Tomoaki and Fernandez-Esparrach, G. and Parsa, Nasim and Byrne, Michael F. and Messmann, Helmut and Ebigbo, Alanna}, title = {Human-Computer Interaction: Impact of Artificial Intelligence on the diagnostic confidence of endoscopists assessing videos of Barrett's esophagus}, series = {Endoscopy}, volume = {56}, journal = {Endoscopy}, number = {S 02}, publisher = {Georg Thieme Verlag}, issn = {1438-8812}, doi = {10.1055/s-0044-1782859}, pages = {79}, abstract = {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.}, language = {en} } @misc{ZellmerRauberProbstetal., author = {Zellmer, Stephan and Rauber, David and Probst, Andreas and Weber, Tobias and Braun, Georg and Nagl, Sandra and R{\"o}mmele, Christoph and Schnoy, Elisabeth and Birzle, Lisa and Aehling, Niklas and Schulz, Dominik Andreas Helmut Otto and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {K{\"u}nstliche Intelligenz als Hilfsmittel zur Detektion der Papilla duodeni major und des papill{\"a}ren Ostiums w{\"a}hrend der ERCP}, series = {Zeitschrift f{\"u}r Gastroenterologie}, volume = {63}, journal = {Zeitschrift f{\"u}r Gastroenterologie}, number = {5}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/s-0045-1806882}, pages = {e295}, abstract = {Einleitung Die Endoskopische Retrograde Cholangiopankreatikographie (ERCP) ist der Goldstandard in der endoskopischen Therapie von Erkrankungen des pankreatobili{\"a}ren Trakts. Allerdings ist sie technisch anspruchsvoll, schwer zu erlernen und mit einer relativ hohen Komplikationsrate assoziiert. Daher soll in der vorliegenden Machbarkeitsstudie gepr{\"u}ft werden, ob mithilfe eines Deeplearning- Algorithmus die Papille und das Ostium zuverl{\"a}ssig detektiert werden k{\"o}nnen und dieser f{\"u}r Endoskopiker, insbesondere in der Ausbildungssituation, ein geeignetes Hilfsmittel darstellen k{\"o}nnte. 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. F{\"u}r den Test des Algorithmus erfolgte eine f{\"u}nffache Kreuzvalidierung. Ergebnisse Auf den 1534 gelabelten Bildern wurden f{\"u}r die Klasse Papille ein F1-Wert von 0,7996, eine Sensitivit{\"a}t von 0,8488 und eine Spezifit{\"a}t von 0,9822 erzielt. F{\"u}r die Klasse Ostium ergaben sich ein F1-Wert von 0,5198, eine Sensitivit{\"a}t von 0,5945 und eine Spezifit{\"a}t von 0,9974. Klassen{\"u}bergreifend (Klasse Papille und Klasse Ostium) betrug der F1-Wert 0,6593, die Sensitivit{\"a}t 0,7216 und f{\"u}r die Spezifit{\"a}t 0,9898. Zusammenfassung In der vorliegenden Machbarkeitsstudie zeigte das neuronale Netz eine hohe Sensitivit{\"a}t und eine sehr hohe Spezifit{\"a}t bei der Identifikation der Papilla duodeni major. Die Detektion des Ostiums erfolgte hingegen mit einer deutlich geringeren Sensitivit{\"a}t. Zuk{\"u}nftig ist eine Erweiterung des Trainingsdatensatzes um Videos und klinische Daten vorgesehen, um die Leistungsf{\"a}higkeit des Netzwerks zu verbessern. Hierdurch k{\"o}nnte langfristig ein geeignetes Assistenzsystem f{\"u}r die ERCP, insbesondere in der Ausbildungssituation etabliert werden.}, language = {de} } @article{RoemmeleMendelBarrettetal., author = {R{\"o}mmele, Christoph and Mendel, Robert and Barrett, Caroline and Kiesl, Hans and Rauber, David and R{\"u}ckert, Tobias and Kraus, Lisa and Heinkele, Jakob and Dhillon, Christine and Grosser, Bianca and Prinz, Friederike and Wanzl, Julia and Fleischmann, Carola and Nagl, Sandra and Schnoy, Elisabeth and Schlottmann, Jakob and Dellon, Evan S. and Messmann, Helmut and Palm, Christoph and Ebigbo, Alanna}, title = {An artificial intelligence algorithm is highly accurate for detecting endoscopic features of eosinophilic esophagitis}, series = {Scientific Reports}, volume = {12}, journal = {Scientific Reports}, publisher = {Nature Portfolio}, address = {London}, doi = {10.1038/s41598-022-14605-z}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-46928}, pages = {10}, abstract = {The endoscopic features associated with eosinophilic esophagitis (EoE) may be missed during routine endoscopy. We aimed to develop and evaluate an Artificial Intelligence (AI) algorithm for detecting and quantifying the endoscopic features of EoE in white light images, supplemented by the EoE Endoscopic Reference Score (EREFS). An AI algorithm (AI-EoE) was constructed and trained to differentiate between EoE and normal esophagus using endoscopic white light images extracted from the database of the University Hospital Augsburg. In addition to binary classification, a second algorithm was trained with specific auxiliary branches for each EREFS feature (AI-EoE-EREFS). The AI algorithms were evaluated on an external data set from the University of North Carolina, Chapel Hill (UNC), and compared with the performance of human endoscopists with varying levels of experience. The overall sensitivity, specificity, and accuracy of AI-EoE were 0.93 for all measures, while the AUC was 0.986. With additional auxiliary branches for the EREFS categories, the AI algorithm (AI-EoEEREFS) performance improved to 0.96, 0.94, 0.95, and 0.992 for sensitivity, specificity, accuracy, and AUC, respectively. AI-EoE and AI-EoE-EREFS performed significantly better than endoscopy beginners and senior fellows on the same set of images. An AI algorithm can be trained to detect and quantify endoscopic features of EoE with excellent performance scores. The addition of the EREFS criteria improved the performance of the AI algorithm, which performed significantly better than endoscopists with a lower or medium experience level.}, language = {en} } @article{RoserMeinikheimMuzalyovaetal., author = {Roser, David and Meinikheim, Michael and Muzalyova, Anna and Mendel, Robert and Palm, Christoph and Probst, Andreas and Nagl, Sandra and Scheppach, Markus W. and R{\"o}mmele, Christoph and Schnoy, Elisabeth and Parsa, Nasim and Byrne, Michael F. and Messmann, Helmut and Ebigbo, Alanna}, title = {Artificial intelligence-assisted endoscopy and examiner confidence : a study on human-artificial intelligence interaction in Barrett's Esophagus (With Video)}, series = {DEN Open}, volume = {6}, journal = {DEN Open}, number = {1}, publisher = {Wiley}, doi = {10.1002/deo2.70150}, pages = {8}, abstract = {Objective Despite high stand-alone performance, studies demonstrate that artificial intelligence (AI)-supported endoscopic diagnostics often fall short in clinical applications due to human-AI interaction factors. This video-based trial on Barrett's esophagus aimed to investigate how examiner behavior, their levels of confidence, and system usability influence the diagnostic outcomes of AI-assisted endoscopy. Methods The present analysis employed data from a multicenter randomized controlled tandem video trial involving 22 endoscopists with varying degrees of expertise. Participants were tasked with evaluating a set of 96 endoscopic videos of Barrett's esophagus in two distinct rounds, with and without AI assistance. Diagnostic confidence levels were recorded, and decision changes were categorized according to the AI prediction. Additional surveys assessed user experience and system usability ratings. Results AI assistance significantly increased examiner confidence levels (p < 0.001) and accuracy. Withdrawing AI assistance decreased confidence (p < 0.001), but not accuracy. Experts consistently reported higher confidence than non-experts (p < 0.001), regardless of performance. Despite improved confidence, correct AI guidance was disregarded in 16\% of all cases, and 9\% of initially correct diagnoses were changed to incorrect ones. Overreliance on AI, algorithm aversion, and uncertainty in AI predictions were identified as key factors influencing outcomes. The System Usability Scale questionnaire scores indicated good to excellent usability, with non-experts scoring 73.5 and experts 85.6. Conclusions Our findings highlight the pivotal function of examiner behavior in AI-assisted endoscopy. To fully realize the benefits of AI, implementing explainable AI, improving user interfaces, and providing targeted training are essential. Addressing these factors could enhance diagnostic accuracy and confidence in clinical practice.}, language = {en} } @article{ScheppachRauberStallhoferetal., author = {Scheppach, Markus W. and Rauber, David and Stallhofer, Johannes and Muzalyova, Anna and Otten, Vera and Manzeneder, Carolin and Schwamberger, Tanja and Wanzl, Julia and Schlottmann, Jakob and Tadic, Vidan and Probst, Andreas and Schnoy, Elisabeth and R{\"o}mmele, Christoph and Fleischmann, Carola and Meinikheim, Michael and Miller, Silvia and M{\"a}rkl, Bruno and Stallmach, Andreas and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {Detection of duodenal villous atrophy on endoscopic images using a deep learning algorithm}, series = {Gastrointestinal Endoscopy}, journal = {Gastrointestinal Endoscopy}, publisher = {Elsevier}, doi = {10.1016/j.gie.2023.01.006}, abstract = {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.}, language = {en} } @misc{MeinikheimMendelProbstetal., author = {Meinikheim, Michael and Mendel, Robert and Probst, Andreas and Scheppach, Markus W. and Schnoy, Elisabeth and Nagl, Sandra and R{\"o}mmele, Christoph and Prinz, Friederike and Schlottmann, Jakob and Golger, Daniela and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {AI-assisted detection and characterization of early Barrett's neoplasia: Results of an Interim analysis}, series = {Endoscopy}, volume = {55}, journal = {Endoscopy}, number = {S02}, publisher = {Thieme}, doi = {10.1055/s-0043-1765437}, pages = {S169}, abstract = {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.}, language = {en} } @misc{MeinikheimMendelProbstetal., author = {Meinikheim, Michael and Mendel, Robert and Probst, Andreas and Scheppach, Markus W. and Nagl, Sandra and Schnoy, Elisabeth and R{\"o}mmele, Christoph and Prinz, Friederike and Schlottmann, Jakob and Messmann, Helmut and Palm, Christoph and Ebigbo, Alanna}, title = {Einfluss von K{\"u}nstlicher Intelligenz auf die Performance von niedergelassenen Gastroenterolog:innen bei der Beurteilung von Barrett-{\"O}sophagus}, series = {Zeitschrift f{\"u}r Gastroenterologie}, volume = {61}, journal = {Zeitschrift f{\"u}r Gastroenterologie}, number = {8}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/s-0043-1771711}, abstract = {Einleitung Die Differenzierung zwischen nicht dysplastischem Barrett-{\"O}sophagus (NDBE) und mit Barrett-{\"O}sophagus assoziierten Neoplasien (BERN) w{\"a}hrend der endoskopischen Inspektion erfordert viel Expertise. Die fr{\"u}he Diagnosestellung ist wichtig f{\"u}r die weitere Prognose des Barrett-Karzinoms. In Deutschland werden Patient:innen mit einem Barrett-{\"O}sophagus (BE) in der Regel im niedergelassenen Sektor {\"u}berwacht. Ziele Ziel ist es, den Einfluss von einem auf K{\"u}nstlicher Intelligenz (KI) basierenden klinischen Entscheidungsunterst{\"u}tzungssystems (CDSS) auf die Performance von niedergelassenen Gastroenterolog:innen (NG) bei der Evaluation von Barrett-{\"O}sophagus (BE) zu untersuchen. Methodik Es erfolgte die prospektive Sammlung von 96 unver{\"a}nderten hochaufl{\"o}senden Videos mit F{\"a}llen von Patient:innen mit histologisch best{\"a}tigtem NDBE und BERN. Alle eingeschlossenen F{\"a}lle 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 Videof{\"a}lle in entweder Gruppe A oder Gruppe B. Gruppe A implizierte eine Evaluation des Falls durch Proband:innen zun{\"a}chst ohne KI und anschließend mit KI als CDSS. In Gruppe B erfolgte die Evaluation in umgekehrter Reihenfolge. Anschließend erfolgte eine zuf{\"a}llige Wiedergabe der so entstandenen Subgruppen im Rahmen des Tests. Ergebnis In diesem Test konnte ein von uns entwickeltes KI-System (Barrett-Ampel) eine Sensitivit{\"a}t von 92,2\%, eine Spezifit{\"a}t von 68,9\% und eine Accuracy von 81,3\% erreichen. Mit der Hilfe von KI verbesserte sich die Sensitivit{\"a}t 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 F{\"a}lle zun{\"a}chst 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.}, language = {de} } @misc{ZellmerRauberProbstetal., author = {Zellmer, Stephan and Rauber, David and Probst, Andreas and Weber, Tobias and Nagl, Sandra and R{\"o}mmele, Christoph and Schnoy, Elisabeth and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {Verwendung k{\"u}nstlicher Intelligenz bei der Detektion der Papilla duodeni major}, series = {Zeitschrift f{\"u}r Gastroenterologie}, volume = {61}, journal = {Zeitschrift f{\"u}r Gastroenterologie}, number = {08}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/s-0043-1772000}, pages = {e539 -- e540}, abstract = {Einleitung Die Endoskopische Retrograde Cholangiopankreatikographie (ERCP) ist der Goldstandard in der Diagnostik und Therapie von Erkrankungen des pankreatobili{\"a}ren Trakts. Jedoch ist sie technisch sehr anspruchsvoll und weist eine vergleichsweise hohe Komplikationsrate auf. Ziele In der vorliegenden Machbarkeitsstudie soll gepr{\"u}ft werden, ob mithilfe eines Deep-learning-Algorithmus die Papille und das Ostium zuverl{\"a}ssig detektiert werden k{\"o}nnen und somit f{\"u}r Endoskopiker mit geringer Erfahrung ein geeignetes Hilfsmittel, insbesondere f{\"u}r die Ausbildungssituation, darstellen k{\"o}nnten. Methodik Wir betrachteten insgesamt 606 Bilddatens{\"a}tze von 65 Patienten. In diesen wurde sowohl die Papilla duodeni major als auch das Ostium segmentiert. Anschließend wurde eine neuronales Netz mittels eines Deep-learning-Algorithmus trainiert. Außerdem erfolgte eine 5-fache Kreuzvaldierung. Ergebnisse Bei einer 5-fachen Kreuzvaldierung auf den 606 gelabelten Daten konnte f{\"u}r die Klasse Papille eine F1-Wert von 0,7908, eine Sensitivit{\"a}t von 0,7943 und eine Spezifit{\"a}t von 0,9785 erreicht werden, f{\"u}r die Klasse Ostium eine F1-Wert von 0,5538, eine Sensitivit{\"a}t von 0,5094 und eine Spezifit{\"a}t von 0,9970 (vgl. [Tab. 1]). Unabh{\"a}ngig von der Klasse zeigte sich gemittelt (Klasse Papille und Klasse Ostium) ein F1-Wert von 0,6673, eine Sensitivit{\"a}t von 0,6519 und eine Spezifit{\"a}t von 0,9877 (vgl. [Tab. 2]). Schlussfolgerung In vorliegende Machbarkeitsstudie konnte das neuronale Netz die Papilla duodeni major mit einer hohen Sensitivit{\"a}t und sehr hohen Spezifit{\"a}t identifizieren. Bei der Detektion des Ostiums war die Sensitivit{\"a}t deutlich geringer. Zuk{\"u}nftig soll das das neuronale Netz mit mehr Daten trainiert werden. Außerdem ist geplant, den Algorithmus auch auf Videos anzuwenden. Somit k{\"o}nnte langfristig ein geeignetes Hilfsmittel f{\"u}r die ERCP etabliert werden.}, language = {de} }