This project represents an innovative breakthrough in the diagnosis and management of Ulcerative Colitis (UC), a form of Inflammatory Bowel Disease (IBD). Designed as a Software as a Medical Device (SaMD), this tool uses artificial intelligence to analyze endoscopic video footage and help doctors identify and grade UC more accurately and efficiently.
Ulcerative Colitis is a chronic condition causing inflammation in the colon, often requiring repeated evaluations to guide treatment. Currently, this process depends on invasive procedures and expert interpretation, which can be subjective and resource-intensive. The proposed system addresses these challenges by using advanced deep learning technology to review endoscopic images and assign a score based on the severity of the disease, following widely accepted clinical guidelines.
The system works by training its AI model on a large dataset of colonoscopy videos, labeled by medical experts. Once trained, it can autonomously assess video footage, identify healthy and diseased tissue, and grade UC severity in real time. This enables doctors to make faster, more consistent, and personalized treatment decisions.
In addition to improving diagnostic accuracy, the system provides continuous monitoring capabilities, helping predict flare-ups and track responses to therapy. Ultimately, the aim is to enhance patient outcomes by enabling earlier interventions, reducing diagnostic uncertainty, and supporting more tailored management plans for people living with UC.