Using AI to find pre-cancerous and cancerous changes in the bowel

Bowel cancer is one of the most serious complications of IBD, patients worry about developing it and clinicians devote large proportions of time and resources trying to detect it early, treat it or ideally prevent it

Dr. Venkat Subramanian, University of Leeds

What this research is looking at 

People living with Crohn’s or Colitis have an increased risk of developing bowel cancer. To help reduce this risk experts recommend having regular camera tests called colonoscopies. Colonoscopies help to look for any pre-cancerous or cancerous changes in the bowel. These tests can take place as often as once every year, to once every five years. The timing depends on whether someone has a higher or lower risk of developing bowel cancer.

A new colonoscopy technique using blue dye helps doctors see cancerous changes. But there is still a risk doctors don’t find all the changes. Sometimes changes aren’t found because doctors don’t notice them. Other times the doctors see the changes but think they are due to Crohn's or Colitis, not cancer.

Artificial Intelligence (AI) is a technology that allows computers to think like humans. AI can learn from information, experiences, and mistakes. This research will create an AI programme that identifies pre-cancerous and cancerous changes. This technology will then be used during colonoscopies. This research will also use AI to find people who are at a higher risk of developing cancer.

What the researchers think this could mean for people with Crohn's and Colitis  

The researchers hope to improve the diagnosis of bowel cancer in people with Crohn’s or Colitis. This research could also inform how often someone should have a colonoscopy. For people at lower risk of developing cancer this could mean fewer colonoscopies. And for people at higher risk this could mean improved monitoring.

Who is leading this research: Dr. Venkat Subramanian, University of Leeds
Our Funding:
24 months 
Official title of application:
Artificial Intelligence assisted identification of dysplastic polyps and high-risk groups during colonoscopic surveillance in patients with colonic Inflammatory Bowel Disease


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