Towards a robust and compact deep learning system for primary detection of early Barrett’s neoplasia: Initial image‐based results of training on a multi‐center retrospectively collected data set
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Le résumé fourni par la source
INTRODUCTION: Endoscopic detection of early neoplasia in Barrett's esophagus is difficult. Computer Aided Detection (CADe) systems may assist in neoplasia detection. The aim of this study was to report the first steps in the development of a CADe system for Barrett's neoplasia and to evaluate its performance when compared with endoscopists. METHODS: This CADe system was developed by a consortium, consisting of the Amsterdam University Medical Center, Eindhoven University of Technology, and 15 international hospitals. After pretraining, the system was trained and validated using 1.713 neoplastic (564 patients) and 2.707 non-dysplastic Barrett's esophagus (NDBE; 665 patients) images. Neoplastic lesions were delineated by 14 experts. The performance of the CADe system was tested on three independent test sets. Test set 1 (50 neoplastic and 150 NDBE images) contained subtle neoplastic lesions representing challenging cases and was benchmarked by 52 general endoscopists. Test set 2 (50 neoplastic and 50 NDBE images) contained a heterogeneous case-mix of neoplastic lesions, representing distribution in clinical practice. Test set 3 (50 neoplastic and 150 NDBE images) contained prospectively collected imagery. The main outcome was correct classification of the images in terms of sensitivity. RESULTS: The sensitivity of the CADe system on test set 1 was 84%. For general endoscopists, sensitivity was 63%, corresponding to a neoplasia miss-rate of one-third of neoplastic lesions and a potential relative increase in neoplasia detection of 33% for CADe-assisted detection. The sensitivity of the CADe system on test sets 2 and 3 was 100% and 88%, respectively. The specificity of the CADe system varied for the three test sets between 64% and 66%. CONCLUSION: This study describes the first steps towards the establishment of an unprecedented data infrastructure for using machine learning to improve the endoscopic detection of Barrett's neoplasia. The CADe system detected neoplasia reliably and outperformed a large group of endoscopists in terms of sensitivity.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Towards a robust and compact deep learning system for primary detection of early Barrett’s neoplasia: Initial image‐based results of training on a multi‐center retrospectively collected data set
- Date Crossref
- 24/04/2023
- Éditeur
- Wiley
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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University of Amsterdam Department of Gastroenterology and Hepatology Amsterdam Gastroenterology pays non établi dans la noticeUniversité ou école supérieure
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Eindhoven University of Technology Department of Electrical Engineering pays non établi dans la noticeUniversité ou école supérieure
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University of Applied Sciences Utrecht pays non établi dans la noticeUniversité ou école supérieure
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Utrecht University pays non établi dans la noticeUniversité ou école supérieure
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University Medical Center Utrecht pays non établi dans la noticeÉtablissement de santé
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St. Antonius Ziekenhuis pays non établi dans la noticeOrganisme public
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Haga Hospital pays non établi dans la noticeÉtablissement de santé
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University Medical Center Groningen pays non établi dans la noticeÉtablissement de santé
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University of Groningen Department of Gastroenterology and Hepatology pays non établi dans la noticeUniversité ou école supérieure
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Isala Department of Gastroenterology and Hepatology pays non établi dans la noticeÉtablissement de santé
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Flevoziekenhuis pays non établi dans la noticeÉtablissement de santé
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Royal Perth Hospital Department of Gastroenterology and Hepatology pays non établi dans la noticeÉtablissement de santé
Department of Gastroenterology and Hepatology Amsterdam Gastroenterology — University of Amsterdam, Department of Electrical Engineering — Eindhoven University of Technology et University of Applied Sciences Utrecht, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.