<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <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>4692</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
    <edition/>
    <issue/>
    <volume>12</volume>
    <type>article</type>
    <publisherName>Nature Portfolio</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-07-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">An artificial intelligence algorithm is highly accurate for detecting endoscopic features of eosinophilic esophagitis</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-46928</identifier>
    <identifier type="doi">10.1038/s41598-022-14605-z</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Christoph Römmele</author>
    <author>Robert Mendel</author>
    <author>Caroline Barrett</author>
    <author>Hans Kiesl</author>
    <author>David Rauber</author>
    <author>Tobias Rückert</author>
    <author>Lisa Kraus</author>
    <author>Jakob Heinkele</author>
    <author>Christine Dhillon</author>
    <author>Bianca Grosser</author>
    <author>Friederike Prinz</author>
    <author>Julia Wanzl</author>
    <author>Carola Fleischmann</author>
    <author>Sandra Nagl</author>
    <author>Elisabeth Schnoy</author>
    <author>Jakob Schlottmann</author>
    <author>Evan S. Dellon</author>
    <author>Helmut Messmann</author>
    <author>Christoph Palm</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>Smart Endoscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>eosinophilic esophagitis</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="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</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/4692/s41598-022-14605-z.pdf</file>
  </doc>
  <doc>
    <id>3507</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>251</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>60</volume>
    <type>conferencepresentation</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace>Stuttgart</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-04-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Einsatz von künstlicher Intelligenz (KI) als Entscheidungsunterstützungssystem für nicht-Experten bei der Beurteilung von Barrett-Ösophagus assoziierten Neoplasien (BERN)</title>
    <abstract language="deu">Einleitung&#13;
Die sichere Detektion und Charakterisierung von Barrett-Ösophagus assoziierten Neoplasien (BERN) stellt selbst für erfahrene Endoskopiker eine Herausforderung dar.&#13;
&#13;
Ziel&#13;
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.&#13;
&#13;
Material und Methodik&#13;
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.&#13;
&#13;
Ergebnisse&#13;
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%.&#13;
&#13;
Zusammenfassung&#13;
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.</abstract>
    <parentTitle language="deu">Zeitschrift für Gastroenterologie</parentTitle>
    <identifier type="doi">10.1055/s-0042-1745653</identifier>
    <enrichment key="ConferenceStatement">49. Jahrestagung der Gesellschaft für Gastroenterologie in Bayern e.V., Freising</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>Michael Meinikheim</author>
    <author>Robert Mendel</author>
    <author>Markus W. Scheppach</author>
    <author>Andreas Probst</author>
    <author>Friederike Prinz</author>
    <author>Tanja Schwamberger</author>
    <author>Jakob Schlottmann</author>
    <author>Stefan Karl Gölder</author>
    <author>Benjamin Walter</author>
    <author>Ingo Steinbrück</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Barrett-Ösophagus</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Künstliche Intelligenz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>psyndex</type>
      <value>Speiseröhrenkrankheit</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="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>3540</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>S39</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>S 01</issue>
    <volume>54</volume>
    <type>conferencepresentation</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-04-19</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">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)</title>
    <abstract language="eng">Aims&#13;
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.&#13;
&#13;
Methods&#13;
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.&#13;
&#13;
Results&#13;
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%.&#13;
&#13;
Conclusions&#13;
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.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="doi">10.1055/s-00000012</identifier>
    <enrichment key="ConferenceStatement">ESGE Days 2022</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>Michael Meinikheim</author>
    <author>Robert Mendel</author>
    <author>Markus W. Scheppach</author>
    <author>Andreas Probst</author>
    <author>Friederike Prinz</author>
    <author>Tanja Schwamberger</author>
    <author>Jakob Schlottmann</author>
    <author>Stefan Karl Gölder</author>
    <author>Benjamin Walter</author>
    <author>Ingo Steinbrück</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>Barrett's Esophagus</value>
    </subject>
    <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>deu</language>
      <type>swd</type>
      <value>Diagnose</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="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>
</export-example>
