In:
PLOS ONE, Public Library of Science (PLoS), Vol. 17, No. 6 ( 2022-6-10), p. e0269728-
Abstract:
Capsule endoscopy has been widely used as a non-invasive diagnostic tool for small or large intestinal lesions. In recent years, automated lesion detection systems using machine learning have been devised. This study aimed to develop an automated system for capsule endoscopic severity in patients with ulcerative colitis along the entire length of the colon using ResNet50. Capsule endoscopy videos from patients with ulcerative colitis were collected prospectively. Each single examination video file was partitioned into four segments: the cecum and ascending colon, transverse colon, descending and sigmoid colon, and rectum. Fifty still pictures (576 × 576 pixels) were extracted from each partitioned video. A patch (128 × 128 pixels) was trimmed from the still picture at every 32-pixel-strides. A total of 739,021 patch images were manually classified into six categories: 0) Mayo endoscopic subscore (MES) 0, 1) MES1, 2) MES2, 3) MES3, 4) inadequate quality for evaluation, and 5) ileal mucosa. ResNet50, a deep learning framework, was trained using 483,644 datasets and validated using 255,377 independent datasets. In total, 31 capsule endoscopy videos from 22 patients were collected. The accuracy rates of the training and validation datasets were 0.992 and 0.973, respectively. An automated evaluation system for the capsule endoscopic severity of ulcerative colitis was developed. This could be a useful tool for assessing topographic disease activity, thus decreasing the burden of image interpretation on endoscopists.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0269728
DOI:
10.1371/journal.pone.0269728.g001
DOI:
10.1371/journal.pone.0269728.g002
DOI:
10.1371/journal.pone.0269728.g003
DOI:
10.1371/journal.pone.0269728.g004
DOI:
10.1371/journal.pone.0269728.g005
DOI:
10.1371/journal.pone.0269728.t001
DOI:
10.1371/journal.pone.0269728.t002
DOI:
10.1371/journal.pone.0269728.t003
DOI:
10.1371/journal.pone.0269728.t004
DOI:
10.1371/journal.pone.0269728.s001
DOI:
10.1371/journal.pone.0269728.s002
DOI:
10.1371/journal.pone.0269728.s003
DOI:
10.1371/journal.pone.0269728.s004
DOI:
10.1371/journal.pone.0269728.s005
DOI:
10.1371/journal.pone.0269728.r001
DOI:
10.1371/journal.pone.0269728.r002
DOI:
10.1371/journal.pone.0269728.r003
DOI:
10.1371/journal.pone.0269728.r004
DOI:
10.1371/journal.pone.0269728.r005
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2267670-3