Artificial intelligence model for analyzing colonic endoscopy images to detect changes associated with irritable bowel syndrome

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, predic...