Endoscopy 2023; 55(01): 44-51
DOI: 10.1055/a-1881-4209
Innovations and brief communications

Artificial intelligence-based diagnosis of abnormalities in small-bowel capsule endoscopy

Zhen Ding*
1   Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
,
Huiying Shi*
1   Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
,
Hang Zhang
2   Ankon Technologies (Wuhan) Co., Ltd, Wuhan, China
,
Hao Zhang
2   Ankon Technologies (Wuhan) Co., Ltd, Wuhan, China
,
1   Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
3   Department of Gastroenterology, the First Affiliated Hospital of Shihezi University School of Medicine, Shihezi 832008, China
,
Kun Zhang
1   Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
,
Sicheng Cai
1   Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
,
Fanhua Ming
2   Ankon Technologies (Wuhan) Co., Ltd, Wuhan, China
,
Xiaoping Xie
1   Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
,
Jun Liu
1   Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
,
Rong Lin
1   Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
› Author Affiliations
National key research and development program of China No. 2017YFC0110003National Natural Science Foundation of ChinaNos. 81770539 and 81974068.


Abstract

Background Further development of deep learning-based artificial intelligence (AI) technology to automatically diagnose multiple abnormalities in small-bowel capsule endoscopy (SBCE) videos is necessary. We aimed to develop an AI model, to compare its diagnostic performance with doctors of different experience levels, and to further evaluate its auxiliary role for doctors in diagnosing multiple abnormalities in SBCE videos.

Methods The AI model was trained using 280 426 images from 2565 patients, and the diagnostic performance was validated in 240 videos.

Results The sensitivity of the AI model for red spots, inflammation, blood content, vascular lesions, protruding lesions, parasites, diverticulum, and normal variants was 97.8 %, 96.1 %, 96.1 %, 94.7 %, 95.6 %, 100 %, 100 %, and 96.4 %, respectively. The specificity was 86.0 %, 75.3 %, 87.3 %, 77.8 %, 67.7 %, 97.5 %, 91.2 %, and 81.3 %, respectively. The accuracy was 95.0 %, 88.8 %, 89.2 %, 79.2 %, 80.8 %, 97.5 %, 91.3 %, and 93.3 %, respectively. For junior doctors, the assistance of the AI model increased the overall accuracy from 85.5 % to 97.9 % (P  < 0.001, Bonferroni corrected), comparable to that of experts (96.6 %, P > 0.0125, Bonferroni corrected).

Conclusions This well-trained AI diagnostic model automatically diagnosed multiple small-bowel abnormalities simultaneously based on video-level recognition, with potential as an excellent auxiliary system for less-experienced endoscopists.

* These authors contributed equally to this work.


Supplementary material



Publication History

Received: 30 November 2021

Accepted after revision: 12 May 2022

Article published online:
05 August 2022

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