A deep learning system for detection of early Barrett's neoplasia: a model development and validation study
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Le résumé fourni par la source
BACKGROUND: Computer-aided detection (CADe) systems could assist endoscopists in detecting early neoplasia in Barrett's oesophagus, which could be difficult to detect in endoscopic images. The aim of this study was to develop, test, and benchmark a CADe system for early neoplasia in Barrett's oesophagus. METHODS: The CADe system was first pretrained with ImageNet followed by domain-specific pretraining with GastroNet. We trained the CADe system on a dataset of 14 046 images (2506 patients) of confirmed Barrett's oesophagus neoplasia and non-dysplastic Barrett's oesophagus from 15 centres. Neoplasia was delineated by 14 Barrett's oesophagus experts for all datasets. We tested the performance of the CADe system on two independent test sets. The all-comers test set comprised 327 (73 patients) non-dysplastic Barrett's oesophagus images, 82 (46 patients) neoplastic images, 180 (66 of the same patients) non-dysplastic Barrett's oesophagus videos, and 71 (45 of the same patients) neoplastic videos. The benchmarking test set comprised 100 (50 patients) neoplastic images, 300 (125 patients) non-dysplastic images, 47 (47 of the same patients) neoplastic videos, and 141 (82 of the same patients) non-dysplastic videos, and was enriched with subtle neoplasia cases. The benchmarking test set was evaluated by 112 endoscopists from six countries (first without CADe and, after 6 weeks, with CADe) and by 28 external international Barrett's oesophagus experts. The primary outcome was the sensitivity of Barrett's neoplasia detection by general endoscopists without CADe assistance versus with CADe assistance on the benchmarking test set. We compared sensitivity using a mixed-effects logistic regression model with conditional odds ratios (ORs; likelihood profile 95% CIs). FINDINGS: Sensitivity for neoplasia detection among endoscopists increased from 74% to 88% with CADe assistance (OR 2·04; 95% CI 1·73-2·42; p<0·0001 for images and from 67% to 79% [2·35; 1·90-2·94; p<0·0001] for video) without compromising specificity (from 89% to 90% [1·07; 0·96-1·19; p=0·20] for images and from 96% to 94% [0·94; 0·79-1·11; ] for video; p=0·46). In the all-comers test set, CADe detected neoplastic lesions in 95% (88-98) of images and 97% (90-99) of videos. In the benchmarking test set, the CADe system was superior to endoscopists in detecting neoplasia (90% vs 74% [OR 3·75; 95% CI 1·93-8·05; p=0·0002] for images and 91% vs 67% [11·68; 3·85-47·53; p<0·0001] for video) and non-inferior to Barrett's oesophagus experts (90% vs 87% [OR 1·74; 95% CI 0·83-3·65] for images and 91% vs 86% [2·94; 0·99-11·40] for video). INTERPRETATION: CADe outperformed endoscopists in detecting Barrett's oesophagus neoplasia and, when used as an assistive tool, it improved their detection rate. CADe detected virtually all neoplasia in a test set of consecutive cases. FUNDING: Olympus.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- A deep learning system for detection of early Barrett's neoplasia: a model development and validation study
- Date Crossref
- 01/12/2023
- Éditeur
- Elsevier BV
- 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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Amsterdam University Medical Centers pays non établi dans la noticeÉtablissement de santé
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University of Amsterdam Department of Gastroenterology and Hepatology 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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St. Antonius Ziekenhuis pays non établi dans la noticeOrganisme public
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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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Haga Hospital 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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OLVG pays non établi dans la noticeÉtablissement de santé
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Curtin University Department of Gastroenterology and Hepatology pays non établi dans la noticeUniversité ou école supérieure
Amsterdam University Medical Centers, Department of Gastroenterology and Hepatology — University of Amsterdam et Department of Electrical Engineering — Eindhoven University of Technology, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.