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. 2024 Jan 2;24(1):10.
doi: 10.1186/s12876-023-03067-w.

A newly developed deep learning-based system for automatic detection and classification of small bowel lesions during double-balloon enteroscopy examination

Yijie Zhu #  1   2   3 , Xiaoguang Lyu #  1   2   3 , Xiao Tao  1   2   3 , Lianlian Wu  1   2   3 , Anning Yin  1   2   3 , Fei Liao  1   2   3 , Shan Hu  4 , Yang Wang  1   2   3 , Mengjiao Zhang  1   2   3 , Li Huang  1   2   3 , Junxiao Wang  1   2   3 , Chenxia Zhang  1   2   3 , Dexin Gong  1   2   3 , Xiaoda Jiang  1   2   3 , Liang Zhao  5   6   7 , Honggang Yu  8   9   10
Affiliations

A newly developed deep learning-based system for automatic detection and classification of small bowel lesions during double-balloon enteroscopy examination

Yijie Zhu et al. BMC Gastroenterol. .

Abstract

Background: Double-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 methods: A 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.

Results: For 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.

Conclusions: We 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.

Keywords: Artificial intelligence; Double-balloon enteroscopy; Small bowel.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Processing flow diagram of ENDOANGEL-DBE
Fig. 2
Fig. 2
Flow chart
Fig. 3
Fig. 3
Misdiagnosed cases of machine or endoscopists. A Erosions misdiagnosed as angioectasia by novices because bile affects observation. B small erosions misdiagnosed as angioectasia by novices. C Angioectasia with red background misdiagnosed as erosion&ulcer by machine and novices. D Pedunculated polyps in the lumen during peristalsis misdiagnosed as diverticulum by machine. E small angioectasia misdiagnosed as erosion& ulcer by machine. F angioectasia in the edge of view misdiagnosed as erosion& ulcer by machine
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