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

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.

Small bowel capsule endoscopy examination and open access database with artificial intelligence: The SEE‐artificial intelligence project

“…Previous research has demonstrated that image classification AI models for single lesions in each small bowel CE image can be highly effective. 15,21 Since the image classification process is based on the whole visual feature of an image, image classification AI models are weak in pointing out multiple types of annotations in the same frame.Previous reports using image classification for CE AI rarely mention instances where different annotation types overlap in the same frame. 22 As shown in this study, CE images often contain multiple pathological lesions within the same frame.…”

“…Previous research has demonstrated that image classification AI models for single lesions in each small bowel CE image can be highly effective 15,21 . Since the image classification process is based on the whole visual feature of an image, image classification AI models are weak in pointing out multiple types of annotations in the same frame.…”

Artificial intelligence in digestive endoscopy: recent advances

“…The sensitivity was other 99%. The second benefit was reducing reading time from 96.6 AE 22.53 MN with conventional reading to 5.9 AE 2.23 MN with AI [23]. As we reported the next coming step is developing with AI robot allowing with the same capsule automatized stomach and small bowel examination.…”

Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review

“…Deeba et al improved the model by skipping one or a few frames from the sequence of bleeding frames [ 33 ]. A study utilized a CNN-based model to screen high-risk suspicious images from CE videos with a focus on high sensitivity but potentially lower specificity [ 34 ]. Using 84 full-length videos, another study [ 35 ] proposed an algorithm that was comparable with the Suspected Blood Indicator (SBI).…”