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 - 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 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 - 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 VL - 56 SP - 641 EP - 649 PB - Georg Thieme Verlag CY - Stuttgart ER - TY - JOUR A1 - Souza Jr., Luis Antonio de A1 - Mendel, Robert A1 - Strasser, Sophia A1 - Ebigbo, Alanna A1 - Probst, Andreas A1 - Messmann, Helmut A1 - Papa, João Paulo A1 - Palm, Christoph T1 - Convolutional Neural Networks for the evaluation of cancer in Barrett’s esophagus: Explainable AI to lighten up the black-box JF - Computers in Biology and Medicine N2 - Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their level of accountability and transparency must be provided in such evaluations. The reliability related to machine learning predictions must be explained and interpreted, especially if diagnosis support is addressed. For this task, the black-box nature of deep learning techniques must be lightened up to transfer its promising results into clinical practice. Hence, we aim to investigate the use of explainable artificial intelligence techniques to quantitatively highlight discriminative regions during the classification of earlycancerous tissues in Barrett’s esophagus-diagnosed patients. Four Convolutional Neural Network models (AlexNet, SqueezeNet, ResNet50, and VGG16) were analyzed using five different interpretation techniques (saliency, guided backpropagation, integrated gradients, input × gradients, and DeepLIFT) to compare their agreement with experts’ previous annotations of cancerous tissue. We could show that saliency attributes match best with the manual experts’ delineations. Moreover, there is moderate to high correlation between the sensitivity of a model and the human-and-computer agreement. The results also lightened that the higher the model’s sensitivity, the stronger the correlation of human and computational segmentation agreement. We observed a relevant relation between computational learning and experts’ insights, demonstrating how human knowledge may influence the correct computational learning. KW - Deep Learning KW - Künstliche Intelligenz KW - Computerunterstützte Medizin KW - Barrett's esophagus KW - Adenocarcinoma KW - Machine learning KW - Explainable artificial intelligence KW - Computer-aided diagnosis Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-20126 SN - 0010-4825 VL - 135 SP - 1 EP - 14 PB - Elsevier ER - TY - JOUR A1 - Mendel, Robert A1 - Rauber, David A1 - Souza Jr., Luis Antonio de A1 - Papa, João Paulo A1 - Palm, Christoph T1 - Error-Correcting Mean-Teacher: Corrections instead of consistency-targets applied to semi-supervised medical image segmentation JF - Computers in Biology and Medicine N2 - Semantic segmentation is an essential task in medical imaging research. Many powerful deep-learning-based approaches can be employed for this problem, but they are dependent on the availability of an expansive labeled dataset. In this work, we augment such supervised segmentation models to be suitable for learning from unlabeled data. Our semi-supervised approach, termed Error-Correcting Mean-Teacher, uses an exponential moving average model like the original Mean Teacher but introduces our new paradigm of error correction. The original segmentation network is augmented to handle this secondary correction task. Both tasks build upon the core feature extraction layers of the model. For the correction task, features detected in the input image are fused with features detected in the predicted segmentation and further processed with task-specific decoder layers. The combination of image and segmentation features allows the model to correct present mistakes in the given input pair. The correction task is trained jointly on the labeled data. On unlabeled data, the exponential moving average of the original network corrects the student’s prediction. The combined outputs of the students’ prediction with the teachers’ correction form the basis for the semi-supervised update. We evaluate our method with the 2017 and 2018 Robotic Scene Segmentation data, the ISIC 2017 and the BraTS 2020 Challenges, a proprietary Endoscopic Submucosal Dissection dataset, Cityscapes, and Pascal VOC 2012. Additionally, we analyze the impact of the individual components and examine the behavior when the amount of labeled data varies, with experiments performed on two distinct segmentation architectures. Our method shows improvements in terms of the mean Intersection over Union over the supervised baseline and competing methods. Code is available at https://github.com/CloneRob/ECMT. KW - Semi-supervised Segmentation KW - Mean-Teacher KW - Pseudo-labels KW - Medical Imaging Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-57790 SN - 0010-4825 N1 - Corresponding author der OTH Regensburg: Robert Mendel VL - 154 IS - March PB - Elsevier 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 - INPR A1 - Allan, Max A1 - Kondo, Satoshi A1 - Bodenstedt, Sebastian A1 - Leger, Stefan A1 - Kadkhodamohammadi, Rahim A1 - Luengo, Imanol A1 - Fuentes, Felix A1 - Flouty, Evangello A1 - Mohammed, Ahmed A1 - Pedersen, Marius A1 - Kori, Avinash A1 - Alex, Varghese A1 - Krishnamurthi, Ganapathy A1 - Rauber, David A1 - Mendel, Robert A1 - Palm, Christoph A1 - Bano, Sophia A1 - Saibro, Guinther A1 - Shih, Chi-Sheng A1 - Chiang, Hsun-An A1 - Zhuang, Juntang A1 - Yang, Junlin A1 - Iglovikov, Vladimir A1 - Dobrenkii, Anton A1 - Reddiboina, Madhu A1 - Reddy, Anubhav A1 - Liu, Xingtong A1 - Gao, Cong A1 - Unberath, Mathias A1 - Kim, Myeonghyeon A1 - Kim, Chanho A1 - Kim, Chaewon A1 - Kim, Hyejin A1 - Lee, Gyeongmin A1 - Ullah, Ihsan A1 - Luna, Miguel A1 - Park, Sang Hyun A1 - Azizian, Mahdi A1 - Stoyanov, Danail A1 - Maier-Hein, Lena A1 - Speidel, Stefanie T1 - 2018 Robotic Scene Segmentation Challenge N2 - In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of exvivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models. However, the limited background variation and simple motion rendered the dataset uninformative in learning about which techniques would be suitable for segmentation in real surgery. In 2017, at the same workshop in Quebec we introduced the robotic instrument segmentation dataset with 10 teams participating in the challenge to perform binary, articulating parts and type segmentation of da Vinci instruments. This challenge included realistic instrument motion and more complex porcine tissue as background and was widely addressed with modfications on U-Nets and other popular CNN architectures [1]. In 2018 we added to the complexity by introducing a set of anatomical objects and medical devices to the segmented classes. To avoid over-complicating the challenge, we continued with porcine data which is dramatically simpler than human tissue due to the lack of fatty tissue occluding many organs. KW - Minimally invasive surgery KW - Robotic KW - Minimal-invasive Chirurgie KW - Robotik Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-50049 UR - https://arxiv.org/abs/2001.11190 ER - TY - JOUR A1 - Ebigbo, Alanna A1 - Mendel, Robert A1 - Probst, Andreas A1 - Manzeneder, Johannes A1 - Souza Jr., Luis Antonio de A1 - Papa, João Paulo A1 - Palm, Christoph A1 - Messmann, Helmut T1 - Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma JF - GuT N2 - Computer-aided diagnosis using deep learning (CAD-DL) may be an instrument to improve endoscopic assessment of Barrett’s oesophagus (BE) and early oesophageal adenocarcinoma (EAC). Based on still images from two databases, the diagnosis of EAC by CAD-DL reached sensitivities/specificities of 97%/88% (Augsburg data) and 92%/100% (Medical Image Computing and Computer-Assisted Intervention [MICCAI] data) for white light (WL) images and 94%/80% for narrow band images (NBI) (Augsburg data), respectively. Tumour margins delineated by experts into images were detected satisfactorily with a Dice coefficient (D) of 0.72. This could be a first step towards CAD-DL for BE assessment. If developed further, it could become a useful adjunctive tool for patient management. KW - Speiseröhrenkrebs KW - Diagnose KW - Computerunterstütztes Verfahren KW - Maschinelles Lernen Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-68 N1 - Corresponding authors: Alanna Ebigbo and Christoph Palm VL - 68 IS - 7 SP - 1143 EP - 1145 PB - British Society of Gastroenterology ER -