As our ability to treat inflammatory bowel disease (IBD) improves, the need for accurate and precise assessments of disease activity and severity has become essential. Therapeutic decisions are made with consideration of patient symptoms, blood and stool tests, and endoscopic evaluation. The development of numerous severity scales such as the Mayo Endoscopic Score (MES) and the Simple Endoscopic Score for Crohn Disease (SES-CD) have attempted to bring reproducible assessments to both the research arena as well as the bedside. Unfortunately, these scores are limited by interobserver variability, even amongst experts, as well as being cumbersome to use, limiting widespread adoption. Advancements in artificial intelligence (AI) have already begun to aid the endoscopist in other areas of endoscopy including the identification and characterization of colonic polyps. Our proposal would develop and validate a machine learning algorithm to assess the colonoscopic disease activity of inflammatory bowel disease in both Crohn’s Disease (CD) and Ulcerative Colitis (UC).
|Effective start/end date||6/15/21 → 10/31/22|
- Virgo Surgical Video Solutions, Inc (Keswani AGMT 8/31/21)
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.