Artificial Intelligence Model for Analyzing Colonic Endoscopy Images to Detect Changes Associated with Irritable Bowel Syndrome

AbstractBackground/AimsIBS is not an organic disease, and the patients typically show no abnormalities in lower gastrointestinal endoscopy. Recently, biofilm formation has been visualized by endoscopy, and the ability of endoscopy to detect microscopic changes due to dysbiosis and microinflammation has been reported. In this study, we investigated whether an Artificial Intelligence (AI) colon image model can detect IBS without biofilsm.MethodsStudy 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). 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, IBS, IBC-C and IBS-D groups, respectively.ResultsThe AUC of the model discriminating between group N and group 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.ConclusionsUsing 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.