Endoscopic prediction of submucosal invasion in Barrett's cancer with the use of artificial intelligence: a pilot study

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Autor(es): dc.contributorUniv Klinikum Augsburg-
Autor(es): dc.contributorOstbayer TH Regensburg OTH Regensburg-
Autor(es): dc.contributorOTH Regensburg-
Autor(es): dc.contributorSana Klinikum Lichtenberg-
Autor(es): dc.contributorAsklepios Klin Barmbek-
Autor(es): dc.contributorRegensburg Univ-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorCatholic Univ Louvain-
Autor(es): dc.contributorSaku Cent Hosp Adv Care Ctr-
Autor(es): dc.contributorKlin Hirslanden-
Autor(es): dc.contributorVet Affairs Med Ctr-
Autor(es): dc.contributorUniv Kansas-
Autor(es): dc.contributorUniv British Columbia-
Autor(es): dc.creatorEbigbo, Alanna-
Autor(es): dc.creatorMendel, Robert-
Autor(es): dc.creatorRueckert, Tobias-
Autor(es): dc.creatorSchuster, Laurin-
Autor(es): dc.creatorProbst, Andreas-
Autor(es): dc.creatorManzeneder, Johannes-
Autor(es): dc.creatorPrinz, Friederike-
Autor(es): dc.creatorMende, Matthias-
Autor(es): dc.creatorSteinbrueck, Ingo-
Autor(es): dc.creatorFaiss, Siegbert-
Autor(es): dc.creatorRauber, David-
Autor(es): dc.creatorSouza, Luis A. de [UNESP]-
Autor(es): dc.creatorPapa, Joao P. [UNESP]-
Autor(es): dc.creatorDeprez, Pierre H.-
Autor(es): dc.creatorOyama, Tsuneo-
Autor(es): dc.creatorTakahashi, Akiko-
Autor(es): dc.creatorSeewald, Stefan-
Autor(es): dc.creatorSharma, Prateek-
Autor(es): dc.creatorByrne, Michael F.-
Autor(es): dc.creatorPalm, Christoph-
Autor(es): dc.creatorMessmann, Helmut-
Data de aceite: dc.date.accessioned2022-02-22T01:05:41Z-
Data de disponibilização: dc.date.available2022-02-22T01:05:41Z-
Data de envio: dc.date.issued2021-06-26-
Data de envio: dc.date.issued2021-06-26-
Data de envio: dc.date.issued2020-11-15-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1055/a-1311-8570-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/210686-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/210686-
Descrição: dc.descriptionBackground The accurate differentiation between T1a and T1b Barrett's-related cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an artificial intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett's cancer on white-light images. Methods Endoscopic images from three tertiary care centers in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) were evaluated using the AI system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett's cancer. Results The sensitivity, specificity, F1 score, and accuracy of the AI system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.74, and 0.71, respectively. There was no statistically significant difference between the performance of the AI system and that of experts, who showed sensitivity, specificity, F1, and accuracy of 0.63, 0.78, 0.67, and 0.70, respectively. Conclusion This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett's cancer. AI scored equally to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and real-life settings. Nevertheless, the correct prediction of submucosal invasion in Barrett's cancer remains challenging for both experts and AI.-
Descrição: dc.descriptionBavarian Academic Forum (BayWISS)-
Descrição: dc.descriptionUniv Klinikum Augsburg, Med Klin 3, Stenglinstr 2, D-86156 Augsburg, Germany-
Descrição: dc.descriptionOstbayer TH Regensburg OTH Regensburg, Regensburg Med Image Comp ReMIC, Regensburg, Germany-
Descrição: dc.descriptionOTH Regensburg, Regensburg Ctr Hlth Sci & Technol RCHST, Regensburg, Germany-
Descrição: dc.descriptionSana Klinikum Lichtenberg, Gastroenterol, Berlin, Germany-
Descrição: dc.descriptionAsklepios Klin Barmbek, Dept Gastroenterol Hepatol & Intervent Endoscopy, Hamburg, Germany-
Descrição: dc.descriptionOTH Regensburg, Regensburg Ctr Biomed Engn RCBE, Regensburg, Germany-
Descrição: dc.descriptionRegensburg Univ, Regensburg, Germany-
Descrição: dc.descriptionSao Paulo State Univ, Dept Comp, Sao Paulo, Brazil-
Descrição: dc.descriptionCatholic Univ Louvain, Clin Univ St Luc, Brussels, Belgium-
Descrição: dc.descriptionSaku Cent Hosp Adv Care Ctr, Nagano, Japan-
Descrição: dc.descriptionKlin Hirslanden, GastroZentrum, Zurich, Switzerland-
Descrição: dc.descriptionVet Affairs Med Ctr, Dept Gastroenterol & Hepatol, Kansas City, MO USA-
Descrição: dc.descriptionUniv Kansas, Sch Med, Kansas City, MO USA-
Descrição: dc.descriptionUniv British Columbia, Vancouver Gen Hosp, Div Gastroenterol, Vancouver, BC, Canada-
Descrição: dc.descriptionSao Paulo State Univ, Dept Comp, Sao Paulo, Brazil-
Formato: dc.format6-
Idioma: dc.languageen-
Publicador: dc.publisherGeorg Thieme Verlag Kg-
Relação: dc.relationEndoscopy-
???dc.source???: dc.sourceWeb of Science-
Título: dc.titleEndoscopic prediction of submucosal invasion in Barrett's cancer with the use of artificial intelligence: a pilot study-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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