DOI: 10.5061/dryad.9s4mw6mkp
title: Data from: Artificial intelligence model for analyzing colonic endoscopy images to detect changes associated with irritable bowel syndrome

description: Background/Aims IBS is not considered to be an organic disease and usually
 shows no abnormality on lower gastrointestinal endoscopy, although biofilm
 formation, dysbiosis, and histological microinflammation have recently
 been reported in patients with IBS. In this study, we investigated whether
 an artificial intelligence (AI) colorectal image model can identify minute
 endoscopic changes, which cannot typically be detected by human
 investigators, that are associated with IBS. Study subjects were
 identified based on electronic medical records and categorized as IBS
 (Group I; n=11), IBS with predominant constipation (IBS-C; Group C; n=12),
 and IBS with predominant diarrhea (IBS-D; Group D; n=12). The study
 subjects had no other diseases. Colonoscopy images from IBS patients and
 from asymptomatic healthy subjects (Group N; n=88) were obtained. Google
 Cloud Platform AutoML Vision (single-label classification) was used to
 construct AI image models to calculate sensitivity, specificity,
 predictive value, and AUC. A total of 2479, 382, 538, and 484 images were
 randomly selected for Groups N, I, C and D, respectively. Results The AUC
 of the model discriminating between Group N and I was 0.95. Sensitivity,
 specificity, positive predictive value, and negative predictive value of
 Group I detection were 30.8%, 97.6%, 66.7%, and 90.2%, respectively. The
 overall AUC of the model discriminating between Groups N, C, and D was
 0.83; sensitivity, specificity, and positive predictive value of Group N
 were 87.5%, 46.2%, and 79.9%, respectively. Conclusions Using the image AI
 model, colonoscopy images of IBS could be discriminated from healthy
 subjects at AUC 0.95. Prospective studies are needed to further validate
 whether this externally validated model has similar diagnostic
 capabilities at other facilities and whether it can be used to determine
 treatment efficacy.
description: For use in
 real-world patients with IBS, patients were identified not by ROME
 criteria, but instead based on disease names recorded for insurance
 purposes between January 2010 and December 2020. These names included
 "Irritable bowel syndrome” (Group I), “constipated irritable bowel
 syndrome” (Group C), and “diarrhea irritable bowel syndrome” (Group D).
 Other diseases such as colorectal cancer, inflammatory bowel disease, and
 eosinophilic gastroenteritis were excluded based on symptoms and results
 of histopathological examinations. However, cases with nonspecific
 inflammatory cell infiltrates that did not meet the diagnostic criteria
 and were being followed up under the respective insurance disease names
 were included in the relevant group. For symptomatic patients, colonoscopy
 was done as part of a workup for changes in bowel habits (e.g., diarrhea),
 and asymptomatic patients had undergone colorectal cancer screening.
 Asymptomatic patients comprised Group N. Colonoscopy images were obtained
 from the endoscopy reporting system. Images were taken by more than 10
 trainees or specialists at a single institution with an Olympus CF-HQ290Z
 or PCF-H290Z colonoscope. The accuracy of the model was improved by
 building the model multiple times after excluding normal light images of
 the terminal ileum, rectal inversion and anus, and narrow band or
 dye-spread images. No biofilm was detected. Images having scores of 2
 (i.e., minor amount of residual staining, small fragments of stool and/or
 opaque liquid, with well-visualized mucosa of colon segments) and 3
 (entire mucosa of the colon segment was well-visualized with no residual
 staining, small fragments of stool and/or opaque liquid) on the Boston
 Bowel Preparation Scale (BBPS) were employed. A total of 20 to 40 images were
 used, with about 5 images for regions in each segment (cecum, ascending,
 transverse, descending, sigmoid colons and rectum) per patient. Groups N,
 I, C, and D had 88, 11, 12, and 12 patients, respectively, for which
 2,479, 382, 538, and 484 images, respectively, were used. The accuracy of
 the model increases with the number of patients, but a minimum of 100
 images afforded a certain degree of accuracy. Thus, the number of patients
 and images used was considered sufficient to construct this
 model. In this
 study, we used annotation and Algorithm Generation using Google Cloud
 AutoML Vision from the Google Cloud Platform (GCP) (Google, Inc.). Four
 labels were defined as Groups N, I, C and D in the training dataset
 (single label classification). Three models were produced that
 differentiated Group I and N, Group N, C, and D, or Group C and D. This
 process was done entirely by a single physician
 (HM).
description: The file can be opened in any imaging software that can open JPG
 files.