ArticleBMC gastroenterology2024
A newly developed deep learning-based system for automatic detection and classification of small bowel lesions during double-balloon enteroscopy examination.
Article in BMC gastroenterology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 5 citations in OpenAlex.
- Gastrointestinal Lesion Detection Using Ensemble Deep Learning Through Global Contextual Information.Bioengineering (Basel, Switzerland) · 2025Article
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination.World journal of gastroenterology · 2025Review
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Authors and funding
16 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDouble-balloon enteroscopy (DBE) is a standard method for diagnosing and treating small bowel disease. However, DBE may yield false-negative results due to oversight or inexperience. We aim to develop a computer-aided diagnostic (CAD) system for the automatic detection and classification of small bowel abnormalities in DBE. DESIGN AND
methodsA total of 5201 images were collected from Renmin Hospital of Wuhan University to construct a detection model for localizing lesions during DBE, and 3021 images were collected to construct a classification model for classifying lesions into four classes, protruding lesion, diverticulum, erosion & ulcer and angioectasia. The performance of the two models was evaluated using 1318 normal images and 915 abnormal images and 65 videos from independent patients and then compared with that of 8 endoscopists. The standard answer was the expert consensus.
resultsFor the image test set, the detection model achieved a sensitivity of 92% (843/915) and an area under the curve (AUC) of 0.947, and the classification model achieved an accuracy of 86%. For the video test set, the accuracy of the system was significantly better than that of the endoscopists (85% vs. 77 ± 6%, p < 0.01). For the video test set, the proposed system was superior to novices and comparable to experts.
conclusionsWe established a real-time CAD system for detecting and classifying small bowel lesions in DBE with favourable performance. ENDOANGEL-DBE has the potential to help endoscopists, especially novices, in clinical practice and may reduce the miss rate of small bowel lesions.
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