New research shows that an Artificial Intelligence (AI) model could predict ulcerative colitis flare-ups and complications after reading biopsies.
In a new paper by the University of Birmingham, researchers supported by the National Institute for Health and Care Research Birmingham Biomedical Research Centre have trialled an AI diagnostic tool which can read digitised biopsies taken during colonoscopy. This could be employed to predict ulcerative colitis flare-ups.
The research is published in Gastroenterology.
What is ulcerative colitis?
Ulcerative colitis is a long-term condition where the colon and rectum become inflamed.
Individuals experiencing an ulcerative colitis flare-up may go weeks or months with very mild symptoms or none at all (remission), followed by periods where the symptoms flare-ups. During a flare-up, some people with ulcerative colitis may experience painful and swollen joints, mouth ulcers, irritated eyes and problems with bones.
This condition is believed to be an autoimmune condition. However, what causes the immune system to behave this way is unclear. Many experts think it’s a combination of genetic and environmental factors. Ulcerative colitis affects around one in every 227 people in the UK and developed at any age but is most often diagnosed in people aged 15 and 25.
Predicting ulcerative colitis flare-ups
The AI model was able to predict the risk of ulcerative colitis flare-ups. The system was trained on existing digitised biopsies and can detect activity related to ulcerative colitis with 89% accuracy for positive results. It was also able to identify markers of inflammation activity and healing in the same area as biopsies were taken with 80% accuracy, similar to human pathologists.
Professor Marietta Iacucci from the Institute of Immunology and Immunotherapy at the University of Birmingham and University College Cork in Ireland, and co-lead author of the paper said: “The power of AI in healthcare is evident in trials like these, where a model can be used to standardise in real-time histological assessment of Ulcerative Colitis disease activity. But most importantly, it provides analytical support and enables clinicians to support those at the greatest risk of relapsing symptoms and disease course.
“Ulcerative Colitis is a complex condition to predict, and developing machine learning-derived systems to make this diagnostic job quicker and more accurate could be a game changer. As models like this further develop, the predictive quality is likely to improve even more, and our paper demonstrates how beneficial such technology could be for clinicians and, crucially patients.”
