AI Detects Hard to Spot Cancerous Colon Lesions

For the study, published last year in Digestive Endoscopy, the research team trained the AI in Odin Vision’s CADDIE system on these flat polyps. CADDIE uses AI during an endoscopy to detect and characterize adenomatous polyps (AP) in real-time. APs are more common and have distinctive tubular features with the growth resembling a mushroom, attached to the colon by a thin stalk.

In comparison flat polyps, including flat lesions and sessile serrated lesions (SSLs), known as ‘subtle advanced neoplasia’, have far fewer distinctive features and are notoriously difficult to detect. They are also more likely to develop into an aggressive cancer, meaning it will spread or grow quickly.

Artificial Intelligence in Colorectal Cancers

To overcome this, researchers upgraded the CADDIE AI algorithm and created four video test datasets, containing images of 173 polyps (consisting of more than 670,000 individual frames), that included a dataset of the most challenging flat polyps.


Researchers say the enhanced AI will help to detect significantly more of the subtle advanced neoplasia group, helping to reduce colorectal (cancer of the colon or rectum, or bowel cancer) cancer – the second leading cause of cancer deaths in the UK after lung cancer.

The CADDIE system, now upgraded, is being used in several NHS hospitals, including UCLH, along with many private hospitals.

Senior author Professor Laurence Lovat (UCL Surgery & Interventional Science) said: “Polyps that are flat, or depressed, are far harder to spot until they are quite large, and the data shows they are more likely to be aggressive.

The artificial intelligence algorithm detected a significantly higher percentage of the flat polyps when compared to expert and trainee colonoscopists, at 79.5% versus 37% and 11.5% respectively.

Lead author Dr Omer Ahmad (Wellcome / EPSRC Centre for Interventional & Surgical Sciences, UCL) said: “We spent considerable time working with expert colonoscopists to develop the video dataset of extremely challenging polyps that could easily have been missed.

“These polyps were incredibly subtle in appearance and highlight the importance of developing novel technologies to assist colonoscopists during the procedure and improve detection rates.

“We hope that this major system upgrade will support clinicians in the fight against this common cancer, which is set to become more prevalent over the next few years.”

Co-author Juana Gonzlez-Bueno (Research Scientist, Odin Vision) added: “It was encouraging to see that AI can double the expert detection rate for these polyps. More importantly, it can increase the detection rate of less experienced colonoscopists by a factor of seven. Overall, the implications are great as it enables all colonoscopist experience levels to surpass expert polyp detection rates.”

Colorectal Cancer and Detection of Polyps

Colorectal cancer is the fourth most common cancer in the UK, with over 41,000 new cases diagnosed each year and around 268,000 people living with the condition.

There are around 16,800 colorectal cancer-related deaths in the UK each year, and this is predicted to increase by 51% over the next 15 years and become more widespread in people under 50.

Colonoscopy is the accepted global gold standard for screening of colorectal cancer (CRC) and most CRCs develop from APs.

During a colonoscopy, a thin pipe with a camera attached is inserted into the bowel, allowing clinicians to view the inner lining and look for anomalies such as APs and flat polyps.

When a polyp is discovered, they are usually removed with surgery the same day.

While expert-level colonoscopists have very good adenoma detection rates (ADR), levels vary greatly between hospitals. It is estimated 25% of polyps are missed during colonoscopy; with each one per cent increase in the ADR correlating to a three per cent decrease in the risk of cancer.

The CADDIE (Computer Aided (AI) Device used in Endoscopy) system was launched in 2020 and helping to significant boost the ADR is hospitals equipped with the technology.

The work was supported by the Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS) at UCL.

Source: Eurekalert

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