TY - CHAP A1 - Weber Nunes, Danilo A1 - Hammer, Michael A1 - Hammer, Simone A1 - Uller, Wibke A1 - Palm, Christoph T1 - Classification of Vascular Malformations Based on T2 STIR Magnetic Resonance Imaging T2 - Bildverarbeitung für die Medizin 2022: Proceedings, German Workshop on Medical Image Computing, Heidelberg, June 26-28, 2022 N2 - Vascular malformations (VMs) are a rare condition. They can be categorized into high-flow and low-flow VMs, which is a challenging task for radiologists. In this work, a very heterogeneous set of MRI images with only rough annotations are used for classification with a convolutional neural network. The main focus is to describe the challenging data set and strategies to deal with such data in terms of preprocessing, annotation usage and choice of the network architecture. We achieved a classification result of 89.47 % F1-score with a 3D ResNet 18. KW - Deep Learning KW - Magnetic Resonance Imaging KW - Vascular Malformations Y1 - 2022 U6 - https://doi.org/10.1007/978-3-658-36932-3_57 SP - 267 EP - 272 PB - Springer Vieweg CY - Wiesbaden ER - TY - JOUR A1 - Knoedler, Leonard A1 - Baecher, Helena A1 - Kauke-Navarro, Martin A1 - Prantl, Lukas A1 - Machens, Hans-Günther A1 - Scheuermann, Philipp A1 - Palm, Christoph A1 - Baumann, Raphael A1 - Kehrer, Andreas A1 - Panayi, Adriana C. A1 - Knoedler, Samuel T1 - Towards a Reliable and Rapid Automated Grading System in Facial Palsy Patients: Facial Palsy Surgery Meets Computer Science JF - Journal of Clinical Medicine N2 - Background: Reliable, time- and cost-effective, and clinician-friendly diagnostic tools are cornerstones in facial palsy (FP) patient management. Different automated FP grading systems have been developed but revealed persisting downsides such as insufficient accuracy and cost-intensive hardware. We aimed to overcome these barriers and programmed an automated grading system for FP patients utilizing the House and Brackmann scale (HBS). Methods: Image datasets of 86 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany, between June 2017 and May 2021, were used to train the neural network and evaluate its accuracy. Nine facial poses per patient were analyzed by the algorithm. Results: The algorithm showed an accuracy of 100%. Oversampling did not result in altered outcomes, while the direct form displayed superior accuracy levels when compared to the modular classification form (n = 86; 100% vs. 99%). The Early Fusion technique was linked to improved accuracy outcomes in comparison to the Late Fusion and sequential method (n = 86; 100% vs. 96% vs. 97%). Conclusions: Our automated FP grading system combines high-level accuracy with cost- and time-effectiveness. Our algorithm may accelerate the grading process in FP patients and facilitate the FP surgeon’s workflow. Y1 - 2022 U6 - https://doi.org/10.3390/jcm11174998 VL - 11 IS - 17 PB - MDPI CY - Basel 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 - 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 - 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 - JOUR A1 - Römmele, Christoph A1 - Mendel, Robert A1 - Barrett, Caroline A1 - Kiesl, Hans A1 - Rauber, David A1 - Rückert, Tobias A1 - Kraus, Lisa A1 - Heinkele, Jakob A1 - Dhillon, Christine A1 - Grosser, Bianca A1 - Prinz, Friederike A1 - Wanzl, Julia A1 - Fleischmann, Carola A1 - Nagl, Sandra A1 - Schnoy, Elisabeth A1 - Schlottmann, Jakob A1 - Dellon, Evan S. A1 - Messmann, Helmut A1 - Palm, Christoph A1 - Ebigbo, Alanna T1 - An artificial intelligence algorithm is highly accurate for detecting endoscopic features of eosinophilic esophagitis JF - Scientific Reports N2 - 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. KW - Artificial Intelligence KW - Smart Endoscopy KW - eosinophilic esophagitis Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-46928 VL - 12 PB - Nature Portfolio CY - London ER - TY - JOUR A1 - Ebigbo, Alanna A1 - Mendel, Robert A1 - Probst, Andreas A1 - Meinikheim, Michael A1 - Byrne, Michael F. A1 - Messmann, Helmut A1 - Palm, Christoph T1 - Multimodal imaging for detection and segmentation of Barrett’s esophagus-related neoplasia using artificial intelligence JF - Endoscopy N2 - The early diagnosis of cancer in Barrett’s esophagus is crucial for improving the prognosis. However, identifying Barrett’s esophagus-related neoplasia (BERN) is challenging, even for experts [1]. Four-quadrant biopsies may improve the detection of neoplasia, but they can be associated with sampling errors. The application of artificial intelligence (AI) to the assessment of Barrett’s esophagus could improve the diagnosis of BERN, and this has been demonstrated in both preclinical and clinical studies [2] [3]. In this video demonstration, we show the accurate detection and delineation of BERN in two patients ([Video 1]). In part 1, the AI system detects a mucosal cancer about 20 mm in size and accurately delineates the lesion in both white-light and narrow-band imaging. In part 2, a small island of BERN with high-grade dysplasia is detected and delineated in white-light, narrow-band, and texture and color enhancement imaging. The video shows the results using a transparent overlay of the mucosal cancer in real time as well as a full segmentation preview. Additionally, the optical flow allows for the assessment of endoscope movement, something which is inversely related to the reliability of the AI prediction. We demonstrate that multimodal imaging can be applied to the AI-assisted detection and segmentation of even small focal lesions in real time. KW - Video KW - Artificial Intelligence KW - Multimodal Imaging Y1 - 2022 U6 - https://doi.org/10.1055/a-1704-7885 VL - 54 IS - 10 PB - Georg Thieme Verlag CY - Stuttgart ET - E-Video 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 -