Introduction: Irritable bowel syndrome (IBS) is a condition in which gastroenterological endoscopists cannot detect anomalies using colonoscopy, yet an artificial intelligence (AI) developed for IBS colonoscopy images has been able to distinguish between IBS and healthy individuals with high accuracy. However, it was unclear which areas the AI identified as abnormal. The aim of this study was to elucidate how AI identifies regions typical of IBS by constructing an additional explainable AI (XAI). METHODS: Colonoscopy images of healthy individuals, patients with constipation-predominant IBS, and patients with diarrhea-predominant IBS, which are available in a repository (https://doi.org/10.5061/dryad.9s4mw6mkp), were used. After setting up a Python environment on a local PC, the XAI models for the three groups were developed. Images not used in the AI construction were then evaluated using XAI. XAI-generated images were independently assessed by two evaluators, H.M. and S.K., to record and reconcile the characteristic differences among the three groups. RESULTS: Images correctly identified as those of healthy individuals by XAI were evaluated as characteristics over the entire image. By contrast, for IBS, only parts of the images were evaluated as characteristic regions. For diarrhea-predominant IBS, regions characterized by clear vascular boundaries, homogeneity or erythematous tones, or narrow and somewhat dark-appearing sections of the intestinal tract were identified. For constipation-predominant IBS, regions characterized by unclear vascular boundaries, faded tones, or dark sections where the end was not visible were identified. CONCLUSION: An XAI for IBS was collaboratively developed by endoscopists and clinical engineers, enabling the visualization of regions characteristic of IBS and healthy individuals. The real-time display of XAI is expected to further advance the elucidation of IBS pathophysiology.
.
