Rapid development of accurate artificial intelligence scoring for colitis disease activity using applied data science techniques.

ABSTRACT: Background and study aims  Scoring endoscopic disease activity in colitis represents a complex task for artificial intelligence (AI), but is seen as a worthwhile goal for clinical and research use cases. To date, development attempts have relied on large datasets, achieving reasonable results when comparing normal to active inflammation, but not when generating subscores for the Mayo Endoscopic Score (MES) or ulcerative colitis endoscopic index of severity (UCEIS). Patients and methods  Using a multi-task learning framework, with frame-by-frame analysis, we developed a machine-learning algorithm (MLA) for UCEIS trained on just 38,124 frames (73 patients with biopsy-proven ulcerative colitis). Scores generated by the MLA were compared to consensus scores from three independent

SUBMITTER: Patel M 

PROVIDER: S-EPMC9010092 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature