An international team of scientists led by Prof Marietta Iacucci, Professor of Gastroenterology UCC/MUH has developed an artificial intelligence (AI) computer-aided diagnosis system that speeds up, simplifies, improves accuracy, and minimises errors in evaluating and predicting outcomes of Ulcerative Colitis (UC).

This new AI based diagnostic system can change how clinicians evaluate biopsies with a faster, less expensive, and objective assessment tool while being adaptable for a wider field of tissue applications in pathology worldwide.  Ulcerative colitis (UC) is a common chronic inflammatory bowel disease (IBD) that induces inflammation and ulcers in the digestive tract.  It can lead to a range of complications, ill health and suffering and has no known cure, although several treatments can help alleviate symptoms and reduce or eliminate inflammation. Clinical assessments use histopathology as the most effective means of detecting and identifying inflammation and remission.

Traditional UC assessment involving microscopic inflammation is typically complex, time and training intensive, expensive and subject to high interobserver variability.  In their analysis, the team used a sample of 535 biopsies drawn from 273 patients spread across 11 different international centres – providing the benefit of a large sample size and helping to optimise data fit and suitability for the computer model. Between 66%-75% of the biopsies were in histologic remission.