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  <doc>
    <id>7261</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>79</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>S 02</issue>
    <volume>56</volume>
    <type>conferencepresentation</type>
    <publisherName>Georg Thieme Verlag</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Human-Computer Interaction: Impact of Artificial Intelligence on the diagnostic confidence of endoscopists assessing videos of Barrett’s esophagus</title>
    <abstract language="eng">Aims &#13;
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.&#13;
&#13;
Methods &#13;
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.&#13;
&#13;
Results &#13;
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).&#13;
&#13;
Conclusions &#13;
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.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="issn">1438-8812</identifier>
    <identifier type="doi">10.1055/s-0044-1782859</identifier>
    <enrichment key="ConferenceStatement">ESGE Days 2024, Berlin, 25.–27.04.2024</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>David Roser</author>
    <author>Michael Meinikheim</author>
    <author>Robert Mendel</author>
    <author>Christoph Palm</author>
    <author>Andreas Probst</author>
    <author>Anna Muzalyova</author>
    <author>Markus W. Scheppach</author>
    <author>Sandra Nagl</author>
    <author>Elisabeth Schnoy</author>
    <author>Christoph Römmele</author>
    <author>Dominik Andreas Helmut Otto Schulz</author>
    <author>Jakob Schlottmann</author>
    <author>Friederike Prinz</author>
    <author>David Rauber</author>
    <author>Tobias Rückert</author>
    <author>Tomoaki Matsumura</author>
    <author>G. Fernandez-Esparrach</author>
    <author>Nasim Parsa</author>
    <author>Michael F. Byrne</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>5429</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2388</pageFirst>
    <pageLast>2390</pageLast>
    <pageNumber/>
    <edition/>
    <issue>12</issue>
    <volume>71</volume>
    <type>article</type>
    <publisherName>BMJ</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-09-16</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Gut</parentTitle>
    <identifier type="doi">10.1136/gutjnl-2021-326470</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-54293</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International</licence>
    <author>Alanna Ebigbo</author>
    <author>Robert Mendel</author>
    <author>Markus W. Scheppach</author>
    <author>Andreas Probst</author>
    <author>Neal Shahidi</author>
    <author>Friederike Prinz</author>
    <author>Carola Fleischmann</author>
    <author>Christoph Römmele</author>
    <author>Stefan Karl Gölder</author>
    <author>Georg Braun</author>
    <author>David Rauber</author>
    <author>Tobias Rückert</author>
    <author>Luis Antonio de Souza Jr.</author>
    <author>João Paulo Papa</author>
    <author>Michael F. Byrne</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial Intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Endoscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Medical Image Computing</value>
    </subject>
    <collection role="ddc" number="004">Datenverarbeitung; Informatik</collection>
    <collection role="ddc" number="617">Chirurgie und verwandte medizinische Fachrichtungen</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/5429/gutjnl-2021-326470.full_supp.pdf</file>
  </doc>
  <doc>
    <id>7281</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>641</pageFirst>
    <pageLast>649</pageLast>
    <pageNumber>9</pageNumber>
    <edition/>
    <issue/>
    <volume>56</volume>
    <type>article</type>
    <publisherName>Georg Thieme Verlag</publisherName>
    <publisherPlace>Stuttgart</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-05-05</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett’s esophagus: a tandem randomized and video trial</title>
    <abstract language="eng">Background &#13;
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).&#13;
&#13;
Methods &#13;
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.&#13;
&#13;
Results &#13;
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.&#13;
&#13;
Conclusion &#13;
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.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="doi">10.1055/a-2296-5696</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-72818</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Michael Meinikheim</author>
    <author>Robert Mendel</author>
    <author>Christoph Palm</author>
    <author>Andreas Probst</author>
    <author>Anna Muzalyova</author>
    <author>Markus W. Scheppach</author>
    <author>Sandra Nagl</author>
    <author>Elisabeth Schnoy</author>
    <author>Christoph Römmele</author>
    <author>Dominik Andreas Helmut Otto Schulz</author>
    <author>Jakob Schlottmann</author>
    <author>Friederike Prinz</author>
    <author>David Rauber</author>
    <author>Tobias Rückert</author>
    <author>Tomoaki Matsumura</author>
    <author>Glòria Fernández-Esparrach</author>
    <author>Nasim Parsa</author>
    <author>Michael F. Byrne</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial Intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Endoscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Medical Image Computing</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/7281/Meinikheim_Mendel_Endoscopy_2024.pdf</file>
  </doc>
  <doc>
    <id>7276</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>S428</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>S 02</issue>
    <volume>56</volume>
    <type>conferencepresentation</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace>Stuttgart</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-05-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Early Esophageal Cancer and the Generalizability of Artificial Intelligence</title>
    <abstract language="eng">Aims &#13;
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).&#13;
&#13;
Methods &#13;
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.&#13;
&#13;
Results &#13;
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.&#13;
&#13;
Conclusions &#13;
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.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="doi">10.1055/s-0044-1783775</identifier>
    <enrichment key="ConferenceStatement">ESGE Days 2024</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Alanna Ebigbo</author>
    <author>David Rauber</author>
    <author>Mousa Ayoub</author>
    <author>Lisa Birzle</author>
    <author>Tomoaki Matsumura</author>
    <author>Andreas Probst</author>
    <author>Ingo Steinbrück</author>
    <author>Sandra Nagl</author>
    <author>Christoph Römmele</author>
    <author>Michael Meinikheim</author>
    <author>Markus W. Scheppach</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>7277</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>S93</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>S 02</issue>
    <volume>56</volume>
    <type>conferencepresentation</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace>Stuttgart</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-05-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Artificial Intelligence (AI) improves endoscopists’ vessel detection during endoscopic submucosal dissection (ESD)</title>
    <abstract language="eng">Aims &#13;
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.&#13;
&#13;
Methods &#13;
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.&#13;
&#13;
Results &#13;
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).&#13;
&#13;
Conclusions &#13;
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.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="doi">10.1055/s-0044-1782891</identifier>
    <enrichment key="ConferenceStatement">ESGE Days 2024</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus W. Scheppach</author>
    <author>Robert Mendel</author>
    <author>David Rauber</author>
    <author>Andreas Probst</author>
    <author>Sandra Nagl</author>
    <author>Christoph Römmele</author>
    <author>Michael Meinikheim</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>7278</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>S439</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>S 02</issue>
    <volume>56</volume>
    <type>conferencepresentation</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace>Stuttgart</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-05-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Procedural phase recognition in endoscopic submucosal dissection (ESD) using artificial intelligence (AI)</title>
    <abstract language="eng">Aims &#13;
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.&#13;
&#13;
Methods &#13;
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.&#13;
&#13;
Results &#13;
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.&#13;
&#13;
Conclusions &#13;
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.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="doi">10.1055/s-0044-1783804</identifier>
    <enrichment key="ConferenceStatement">ESGE Days 2024</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus W. Scheppach</author>
    <author>Danilo Weber Nunes</author>
    <author>X. Arizi</author>
    <author>David Rauber</author>
    <author>Andreas Probst</author>
    <author>Sandra Nagl</author>
    <author>Christoph Römmele</author>
    <author>Michael Meinikheim</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>6039</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>S165</pageNumber>
    <edition/>
    <issue>S02</issue>
    <volume>55</volume>
    <type>conferencepresentation</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-05-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Performance comparison of a deep learning algorithm with endoscopists in the detection of duodenal villous atrophy (VA)</title>
    <abstract language="eng">Aims &#13;
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.&#13;
&#13;
Methods&#13;
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”.&#13;
&#13;
Results&#13;
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.&#13;
&#13;
Conclusions&#13;
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.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="doi">10.1055/s-0043-1765421</identifier>
    <enrichment key="ConferenceStatement">ESGE Days 2023</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus W. Scheppach</author>
    <author>David Rauber</author>
    <author>Johannes Stallhofer</author>
    <author>Anna Muzalyova</author>
    <author>Vera Otten</author>
    <author>Carolin Manzeneder</author>
    <author>Tanja Schwamberger</author>
    <author>Julia Wanzl</author>
    <author>Jakob Schlottmann</author>
    <author>Vidan Tadic</author>
    <author>Andreas Probst</author>
    <author>Elisabeth Schnoy</author>
    <author>Christoph Römmele</author>
    <author>Carola Fleischmann</author>
    <author>Michael Meinikheim</author>
    <author>Silvia Miller</author>
    <author>Bruno Märkl</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <collection role="ddc" number="61">Medizin und Gesundheit</collection>
    <collection role="ddc" number="004">Datenverarbeitung; Informatik</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>8056</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>S511</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>S 02</issue>
    <volume>57</volume>
    <type>conferencepresentation</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace>Stuttgart</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-04-28</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Single frame workflow recognition during endoscopic submucosal dissection (ESD) using artificial intelligence (AI)</title>
    <abstract language="eng">Aims &#13;
Precise surgical phase recognition and evaluation may improve our understanding of complex endoscopic procedures. Furthermore, quality control measurements and endoscopy training could benefit from objective descriptions of surgical phase distributions. Therefore, we aimed to develop an artificial intelligence algorithm for frame-by-frame operational phase recognition during endoscopic submucosal dissection (ESD).&#13;
&#13;
Methods &#13;
Full length ESD-videos from 31 patients comprising 6.297.782 single images were collected retrospectively. Videos were annotated on a frame-by-frame basis for the operational macro-phases diagnostics, marking, injection, dissection and bleeding. Further subphases were the application of electrical current, visible injection of fluid into the submucosal space and scope manipulation, leading to 11 phases in total. 4.975.699 frames (21 patients) were used for training of a video swin transformer using uniform frame sampling for temporal information. Hyperparameter tuning was performed with 897.325 further frames (6 patients), while 424.758 frames (4 patients) were used for validation.&#13;
&#13;
Results &#13;
The overall F1 scores on the test dataset for the macro-phases and all 11 phases were 0.96 and 0.90, respectively. The recall values for diagnostics, marking, injection, dissection and bleeding were 1.00, 1.00, 0.95, 0.96 and 0.93, respectively.&#13;
&#13;
Conclusions &#13;
The algorithm classified operational phases during ESD with high accuracy. A precise evaluation of phase distribution may allow for the development of objective quality metrics for quality control and training.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="doi">10.1055/s-0045-1806324</identifier>
    <enrichment key="ConferenceStatement">ESGE Days 2025</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus W. Scheppach</author>
    <author>Danilo Weber Nunes</author>
    <author>X. Arizi</author>
    <author>David Rauber</author>
    <author>Andreas Probst</author>
    <author>Sandra Nagl</author>
    <author>Christoph Römmele</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>5777</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-02-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Detection of duodenal villous atrophy on endoscopic images using a deep learning algorithm</title>
    <abstract language="eng">Background and aims&#13;
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.&#13;
&#13;
Methods&#13;
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.&#13;
&#13;
Results&#13;
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.&#13;
&#13;
Conclusion&#13;
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.</abstract>
    <parentTitle language="eng">Gastrointestinal Endoscopy</parentTitle>
    <identifier type="doi">10.1016/j.gie.2023.01.006</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Kostentraeger">2071855</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Markus W. Scheppach</author>
    <author>David Rauber</author>
    <author>Johannes Stallhofer</author>
    <author>Anna Muzalyova</author>
    <author>Vera Otten</author>
    <author>Carolin Manzeneder</author>
    <author>Tanja Schwamberger</author>
    <author>Julia Wanzl</author>
    <author>Jakob Schlottmann</author>
    <author>Vidan Tadic</author>
    <author>Andreas Probst</author>
    <author>Elisabeth Schnoy</author>
    <author>Christoph Römmele</author>
    <author>Carola Fleischmann</author>
    <author>Michael Meinikheim</author>
    <author>Silvia Miller</author>
    <author>Bruno Märkl</author>
    <author>Andreas Stallmach</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>celiac disease</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>villous atrophy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>endoscopy detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>artificial intelligence</value>
    </subject>
    <collection role="ddc" number="00">Informatik, Wissen, Systeme</collection>
    <collection role="ddc" number="61">Medizin und Gesundheit</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>7948</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace>Stuttgart</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-02-15</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Artificial intelligence improves submucosal vessel detection during third space endoscopy</title>
    <abstract language="eng">Background and study aims: While artificial intelligence (AI) shows high potential in decision support for diagnostic gastrointestinal endoscopy, its role in therapeutic endoscopy remains unclear. Third space endoscopic procedures pose the risk of intraprocedural bleeding. Therefore, we aimed to develop an AI algorithm for intraprocedural blood vessel detection. Patients and Methods: Using a test dataset with 101 standardized video clips containing 200 predefined submucosal blood vessels, 19 endoscopists were evaluated for the vessel detection rate (VDR) and time (VDT) with and without support of an AI algorithm. Test subjects were grouped according to experience in ESD. Results: With AI support, endoscopists VDR increased from 56.4% [CI 54.1–58.6] to 72.4% [CI 70.3–74.4]. Endoscopists‘ VDT dropped from 6.7sec [CI 6.2-7.1] to 5.2sec [CI 4.8-5.7]. False positive (FP) readings appeared in 4.5% of frames and were marked significantly shorter than true positives (6.0sec [CI 5.28-6.70] vs. 0.7sec [CI 0.55-0.87]). Conclusions: AI improved the vessel detection rate and time of endoscopists during third space endoscopy. While these data need to be corroborated by clinical trials, AI may prove to be an invaluable tool for the improvement of endoscopic interventions.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="doi">10.1055/a-2534-1164</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus W. Scheppach</author>
    <author>Robert Mendel</author>
    <author>Anna Muzalyova</author>
    <author>David Rauber</author>
    <author>Andreas Probst</author>
    <author>Sandra Nagl</author>
    <author>Christoph Römmele</author>
    <author>Hon Chi Yip</author>
    <author>Louis Ho Shing Lau</author>
    <author>Stefan Karl Gölder</author>
    <author>Arthur Schmidt</author>
    <author>Konstantinos Kouladouros</author>
    <author>Mohamed Abdelhafez</author>
    <author>Benjamin M. Walter</author>
    <author>Michael Meinikheim</author>
    <author>Philip Wai Yan Chiu</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial Intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Third Space Endoscopy</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>2025</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>S 01</issue>
    <volume>53</volume>
    <type>conferencepresentation</type>
    <publisherName>Georg Thieme Verlag</publisherName>
    <publisherPlace>Stuttgart</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-07-30</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Detection Of Celiac Disease Using A Deep Learning Algorithm</title>
    <abstract language="eng">Aims &#13;
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.&#13;
&#13;
Methods &#13;
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.&#13;
&#13;
Results &#13;
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.&#13;
&#13;
Conclusions &#13;
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.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="doi">10.1055/s-0041-1724970</identifier>
    <note>Digital poster exhibition</note>
    <enrichment key="ConferenceStatement">ESGE Days 2021</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus W. Scheppach</author>
    <author>David Rauber</author>
    <author>Robert Mendel</author>
    <author>Christoph Palm</author>
    <author>Michael F. Byrne</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Celiac Disease</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep Learning</value>
    </subject>
    <collection role="ddc" number="000">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="ddc" number="610">Medizin und Gesundheit</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>3380</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>115</pageFirst>
    <pageLast>120</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer Vieweg</publisherName>
    <publisherPlace>Wiesbaden</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-04-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Analysis of Celiac Disease with Multimodal Deep Learning</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Bildverarbeitung für die Medizin 2022: Proceedings, German Workshop on Medical Image Computing, Heidelberg, June 26-28, 2022</parentTitle>
    <identifier type="doi">10.1007/978-3-658-36932-3_25</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>David Rauber</author>
    <author>Robert Mendel</author>
    <author>Markus W. Scheppach</author>
    <author>Alanna Ebigbo</author>
    <author>Helmut Messmann</author>
    <author>Christoph Palm</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Endoscopy</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="ddc" number="6">Technik, Medizin, angewandte Wissenschaften</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCBE">Regensburg Center of Biomedical Engineering - RCBE</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>8499</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>e612</pageFirst>
    <pageLast>e613</pageLast>
    <pageNumber/>
    <edition/>
    <issue>08</issue>
    <volume>63</volume>
    <type>conferencepresentation</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace>Stuttgart</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-09-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Künstliche Intelligenz-basierte Erkennung von interventionellen Phasen bei der endoskopischen Submukosadissektion</title>
    <abstract language="deu">Einleitung: Die endoskopische Submukosadissektion (ESD) ist ein komplexes endoskopisches Verfahren, das technische Expertise erfordert. Objektive Methoden zur Analyse von interventionellen Abläufen bei ESD könnten für Qualitätssicherung und Ausbildung, wie auch eine automatische Befunderstellung von Nutzen sein.&#13;
&#13;
Ziele: In dieser Studie wurde ein KI-Algorithmus für die Erkennung und Klassifizierung der interventionellen Phasen der ESD entwickelt, um die technische Basis für eine standardisierte Leistungsbewertung und automatische Befunderstellung zu schaffen.&#13;
&#13;
Methodik: Vollständige ESD-Videoaufnahmen von 49 Patienten wurden retrospektiv zusammengestellt. Der Datensatz umfasste 6.390.151 Einzelbilder, die alle für die folgenden interventionellen Phasen annotiert wurden: Diagnostik, Markierung, Injektion, Dissektion und Hämostase. 3.973.712 Bilder (28 Patienten) wurden für das Training eines Video-Swin-Transformers genutzt. Dabei wurde temporale Information durch standardisierte BIldextraktion in festgelegten zeitlichen Abständen zum analysierten Bild inkorporiert. 2.416.439 separate Bilder (21 Patienten) wurden für eine interne Validierung genutzt.&#13;
&#13;
Ergebnis: Bei der internen Evaluation erreichte das System insgesamt einen F1-Wert von 0,88. Es wurden F1-Werte von 0,99, 0,89, 0,89, 0,91 und 0,52 für Diagnostik, Markierung, Injektion, Dissektion bzw. Blutungsmanagement gemessen. Die Sensitivitäten für dieselben Parameter betrugen 1,00, 0,80, 0,94, 0,89 und 0,67, die Spezifitäten lagen bei 1,00, 1,00, 0,98, 0,88 und 0,93. Positive prädiktive Werte wurden mit 0,98, 1,00, 0,85, 0,94 und 0,43 gemessen.&#13;
&#13;
Schlussfolgerung: In dieser vorläufigen Studie zeigte ein KI-Algorithmus eine hohe Leistungsfähigkeit für die Einzelbild-Erkennung von Verfahrensphasen während der ESD. Die vergleichsweise niedrige Leistung für die Blutungsphase wurde auf das seltene Auftreten von Blutungsepisoden im Trainingsdatensatz zurückgeführt, der zu diesem Zeitpunkt nur Videos in voller Länge umfasste. Die zukünftige Entwicklung des Algorithmus wird sich auf die Reduzierung von Klassenungleichgewichten durch selektive Annotationsprotokolle konzentrieren.</abstract>
    <parentTitle language="deu">Zeitschrift für Gastroenterologie</parentTitle>
    <identifier type="doi">10.1055/s-0045-1811093</identifier>
    <enrichment key="ConferenceStatement">79. Jahrestagung der DGVS mit Sektion Endoskopie Jahrestagung der Deutschen Gesellschaft für Allgemein- und Viszeralchirurgie mit den Arbeitsgemeinschaften der DGAV und Jahrestagung der CACP. - Viszeralmedizin 2025; 15-20. September 2025</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus W. Scheppach</author>
    <author>Danilo Weber Nunes</author>
    <author>David Rauber</author>
    <author>X. Arizi</author>
    <author>Andreas Probst</author>
    <author>Sandra Nagl</author>
    <author>Christoph Römmele</author>
    <author>Alanna Ebigbo</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
    <collection role="DFGFachsystematik" number="3">Lebenswissenschaften</collection>
    <collection role="othforschungsschwerpunkt" number="">Gesundheit und Soziales</collection>
  </doc>
  <doc>
    <id>8500</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>8</issue>
    <volume>63</volume>
    <type>conferencepresentation</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-09-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Instrumentenerkennung während der endoskopischen Submukosadissektion mittels künstlicher Intelligenz</title>
    <abstract language="deu">Einleitung: Die endoskopische Submukosadissektion (ESD) ist eine komplexe Technik zur Resektion gastrointestinaler Frühneoplasien. Dabei werden für die verschiedenen Schritte der Intervention spezifische endoskopische Instrumente verwendet. Die präzise und automatische Erkennung und Abgrenzung der verwendeten Instrumente (Injektionsnadeln, elektrochirurgische Messer mit unterschiedlichen Konfigurationen, hämostatische Zangen) könnte wertvolle Informationen über den Fortschritt und die Verfahrensmerkmale der ESD liefern und eine automatische standardisierte Berichterstattung ermöglichen.&#13;
&#13;
Ziele: Ziel dieser Studie war die Entwicklung eines KI-Algorithmus zur Erkennung und Delineation von endoskopischen Instrumenten bei der ESD.&#13;
&#13;
Methodik: 17 ESD-Videos (9×rektal, 5×ösophageal, 3×gastrisch) wurden retrospektiv zusammengestellt. Auf 8530 Einzelbilder dieser Videos wurden durch 2 Studienmitarbeiter die folgenden Klassen eingezeichnet: Hakenmesser – Spitze, Hakenmesser – Katheter, Nadelmesser – Spitze und – Katheter, Injektionsnadel -Spitze und – Katheter sowie hämostatische Zange – Spitze und – Katheter. Der annotierte Datensatz wurde zum Training eines DeepLabV3+-Deep-Learning-Algorithmus mit ConvNeXt-Backbone zur Erkennung und Abgrenzung der genannten Klassen verwendet. Die Evaluation erfolgte durch 5-fache interne Kreuzvalidierung.&#13;
&#13;
Ergebnis: Die Validierung auf Einzelpixelbasis ergab insgesamt einen F1-Score von 0,80, eine Sensitivität von 0,81 und eine Spezifität von 1,00. Es wurden F1-Scores von 1,00, 0,97, 0,80, 0,98, 0,85, 0,97, 0,80, 0,51 bzw. 0,85 für die Klassen Hakenmesser – Katheter und – Spitze, Nadelmesser – Katheter und – Spitze, Injektionsnadel – Katheter und – Spitze, hämostatische Zange – Katheter und – Spitze gemessen.&#13;
&#13;
Schlussfolgerung: In dieser Studie wurden die wichtigsten endoskopischen Instrumente, die während der ESD verwendet werden, mit hoher Genauigkeit erkannt. Die geringere Leistung bei der hämostatische Zange – Katheter kann auf die Unterrepräsentation dieser Klassen in den Trainingsdaten zurückgeführt werden. Zukünftige Studien werden sich auf die Erweiterung der Instrumentenklassen sowie auf die Ausbalancierung der Trainingsdaten konzentrieren.</abstract>
    <parentTitle language="deu">Zeitschrift für Gastroenterologie</parentTitle>
    <identifier type="doi">10.1055/s-0045-1811092</identifier>
    <enrichment key="ConferenceStatement">79. Jahrestagung der DGVS mit Sektion Endoskopie Jahrestagung der Deutschen Gesellschaft für Allgemein- und Viszeralchirurgie mit den Arbeitsgemeinschaften der DGAV und Jahrestagung der CACP. - Viszeralmedizin 2025; 15.-20. September 2025</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus W. Scheppach</author>
    <author>David Rauber</author>
    <author>C. Zingler</author>
    <author>Danilo Weber Nunes</author>
    <author>Andreas Probst</author>
    <author>Christoph Römmele</author>
    <author>Sandra Nagl</author>
    <author>Alanna Ebigbo</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
    <collection role="DFGFachsystematik" number="3">Lebenswissenschaften</collection>
    <collection role="othforschungsschwerpunkt" number="">Gesundheit und Soziales</collection>
  </doc>
  <doc>
    <id>6041</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>S53</pageFirst>
    <pageLast>S54</pageLast>
    <pageNumber/>
    <edition/>
    <issue>S02</issue>
    <volume>55</volume>
    <type>conferencepresentation</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-05-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Real-time detection and delineation of tissue during third-space endoscopy using artificial intelligence (AI)</title>
    <abstract language="eng">Aims &#13;
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.&#13;
&#13;
Methods &#13;
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.&#13;
&#13;
Results &#13;
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).&#13;
&#13;
Conclusions &#13;
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.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="doi">10.1055/s-0043-1765128</identifier>
    <enrichment key="ConferenceStatement">ESGE Days 2023</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus W. Scheppach</author>
    <author>Robert Mendel</author>
    <author>Andreas Probst</author>
    <author>David Rauber</author>
    <author>Tobias Rückert</author>
    <author>Michael Meinikheim</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Speiseröhrenkrankheit</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Künstliche Intelligenz</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial Intelligence</value>
    </subject>
    <collection role="ddc" number="61">Medizin und Gesundheit</collection>
    <collection role="ddc" number="000">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>7799</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>e830</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>09</issue>
    <volume>62</volume>
    <type>conferencepresentation</type>
    <publisherName>Georg Thieme Verlag KG</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Künstliche Intelligenz erhöht die Gefäßerkennung von Endoskopikern bei third space Endoskopie</title>
    <abstract language="deu">Einleitung: Künstliche Intelligenz (KI)-Algorithmen unterstützen Endoskopiker bei der Erkennung und Charakterisierung von Kolonpolypen in der klinischen Praxis und führen zu einer Erhöhung der Adenomdetektionsrate. Auch bei therapeutischen Maßnahmen wie der endoskopischen Submukosadissektion (ESD) könne relevante anatomische Strukturen durch KI mit hoher Genauigkeit erkannt und im endoskopischen Bild in Echtzeit markiert werden. Der Effekt einer solchen Applikation auf die Gefäßdetektion von Endoskopikern ist bislang nicht erforscht.&#13;
Ziele: &#13;
In dieser Studie wurde der Effekt eines KI-Algorithmus zur Echtzeit-Gefäßmarkierung bei ESD auf die Gefäßdetektionsrate von Endoskopikern untersucht.&#13;
Methodik: &#13;
59 third space Endoskopievideos wurde aus der Datenbank des Universitätsklinikums Augsburg extrahiert. Auf 5470 Einzelbildern dieser Untersuchungen wurde submukosale Blutgefäße annotiert. Zusammen mit weiteren 179681 unmarkierten Bildern wurde ein DeepLabV3+ neuronales Netzwerk mit einer semi-supervised learning Methode darin trainiert, submukosale Blutgefäße auf dem endoskopischen Bild zu erkennen und in Echtzeit einzuzeichnen. Anhand eines Videotests mit 101 Videoclips und 200 vordefinierten Blutgefäßen wurden 19 Endoskopiker mit und ohne KI Unterstützung getestet.&#13;
Ergebnis: &#13;
Der Algorithmus erkannte in dem Videotest 93.5% der Gefäße in einer Detektionszeit von im Median 0,3 Sekunden. Die Gefäßdetektionsrate von Endoskopikern erhöhte sich durch KI Unterstützung von 56,4% auf 72,4% (p&lt;0.001). Die Gefäßdetektionszeit reduzierte sich durch KI-Unterstützung von 6,7 auf 5.2 Sekunden (p&lt;0.001). Der Algorithmus zeigte eine Rate an falsch positiven Detektionen in 4.5% der Einzelbilder. Falsch positiv erkannte Strukturen wurde kürzer detektiert, als richtig positive (0.7 und 6.0 Sekunden, p&lt;0.001).&#13;
Schlussfolgerung: &#13;
KI Unterstützung führte zu einer erhöhten Gefäßdetektionsrate und schnelleren Gefäßdetektionszeit von Endoskopikern. Ein möglicher klinischer Effekt auf die intraprozedurale Komplikationsrate oder Operationszeit könnte in prospektiven Studien ermittelt werden.</abstract>
    <parentTitle language="deu">Zeitschrift für Gastroenterologie</parentTitle>
    <identifier type="doi">10.1055/s-0044-1790087</identifier>
    <enrichment key="opus.import.date">2024-11-18T10:16:05+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="ConferenceStatement">78. Jahrestagung der DGVS mit Sektion Endoskopie Jahrestagung der Deutschen Gesellschaft für Allgemein- und Viszeralchirurgie mit den Arbeitsgemeinschaften der DGAV und Jahrestagung der CACP. - Viszeralmedizin 2024. - 9/30/2024 - 10/5/2024</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus W. Scheppach</author>
    <author>Robert Mendel</author>
    <author>Anna Muzalyova</author>
    <author>David Rauber</author>
    <author>Andreas Probst</author>
    <author>Sandra Nagl</author>
    <author>Christoph Römmele</author>
    <author>Hon Chi Yip</author>
    <author>Louis Ho Shing Lau</author>
    <author>Stefan Karl Gölder</author>
    <author>Arthur Schmidt</author>
    <author>Konstantinos Kouladouros</author>
    <author>Mohamed Abdelhafez</author>
    <author>B. Walter</author>
    <author>Michael Meinikheim</author>
    <author>Philip Wai Yan Chiu</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>7798</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>e828</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>09</issue>
    <volume>62</volume>
    <type>conferencepresentation</type>
    <publisherName>Georg Thieme Verlag KG</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Intraoperative Phasenerkennung bei endoskopischer Submukosadissektion mit Hilfe von künstlicher Intelligenz</title>
    <abstract language="deu">Einleitung: &#13;
Künstliche Intelligenz (KI) wird in der Endoskopie des Gastrointestinaltraktes zur Erkennung und Charakterisierung von Kolonpolypen eingesetzt. Die Rolle von KI bei therapeutischen Maßnahmen wurde noch nicht eingehend untersucht. Eine intraprozedurale Phasenerkennung bei endoskopischer Submukoasdissektion (ESD) könnte die Erhebung von Qualitätsindikatoren ermöglichen. Weiterhin könnte diese Technologie zu einem tieferen Verständnis über die Eigenschaften der Prozedur führen und weiterführende Applikationen zur automatischen Dokumentation oder standardisiertem Training vorbereiten.&#13;
Ziele: Ziel dieser Studie war die Entwicklung eines KI Algorithmus zur intraprozeduralen Phasenerkennung bei endoskopischer Submukosadissektion.&#13;
Methodik: &#13;
2071546 Einzelbilder aus 27 ESD Videos in voller Länge wurden für die übergeordneten Klassen Diagnostik, Markierung, Nadelinjektion, Dissektion und Blutung, sowie die untergeordneten Klassen Endoskop-Manipulation, Injektion und Applikation von elektrischem Strom annotiert. Mit einem Trainingsdatensatz (898440 Einzelbilder, 17 ESDs) wurde ein Video Swin Transformer mit uniformer Stichprobenentnahme trainiert und intern validiert (769523 Einzelbilder, 6 ESDs). Neben der internen Validierung wurde der Algorithmus anhand von einem separaten Testdatensatz (403583 Einzelbilder, 4 ESDs) evaluiert.&#13;
Ergebnis: &#13;
Der F1 Score des Algorithmus für alle Klassen lag in der internen Validierung bei 83%, in dem separaten Test bei 90%. Anhand des separaten Tests wurden true positive (TP)-Raten für Diagnostik, Markierung, Nadelinjektion, Dissektion und Blutung von 100%, 100%, 96%, 97% und 93% ermittelt. Für Endoskopmanipulation, Injektion und Applikation von Elektrizität lagen die TP-Raten bei 92%, 98% und 91%.&#13;
Schlussfolgerung: &#13;
Der entwickelte Algorithmus klassifizierte ESD Videos in voller Länge und anhand jedes einzelnen Bildes mit hoher Genauigkeit. Zukünftige Forschungsvorhaben könnten intraoperative Qualitätsindikatioren auf Basis dieser Informationen entwickeln und eine automatisierte Dokumentation ermöglichen.</abstract>
    <parentTitle language="deu">Zeitschrift für Gastroenterologie</parentTitle>
    <identifier type="doi">10.1055/s-0044-1790084</identifier>
    <enrichment key="opus.import.date">2024-11-18T10:16:05+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="ConferenceStatement">78. Jahrestagung der DGVS mit Sektion Endoskopie Jahrestagung der Deutschen Gesellschaft für Allgemein- und Viszeralchirurgie mit den Arbeitsgemeinschaften der DGAV und Jahrestagung der CACP. - Viszeralmedizin 2024. - 9/30/2024 - 10/5/2024</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus W. Scheppach</author>
    <author>Danilo Weber Nunes</author>
    <author>X. Arizi</author>
    <author>David Rauber</author>
    <author>Andreas Probst</author>
    <author>Sandra Nagl</author>
    <author>Christoph Römmele</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
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