Journal
JMIR MEDICAL INFORMATICS
Volume 9, Issue 12, Pages -Publisher
JMIR PUBLICATIONS, INC
DOI: 10.2196/25328
Keywords
artificial intelligence; colonoscopy; adenoma; real-time computer-aided detection; colonic polyp
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By utilizing artificial intelligence methods to improve the quality of colonoscopy, all polyps with malignant potential can be removed, reducing cancer incidence. The integration of deep learning methodology with CADe systems has the potential to increase adenoma detection rates in bowel cancer screening.
The adenoma detection rate is the constant subject of research and the main marker of quality in bowel cancer screening. However, by improving the quality of endoscopy via artificial intelligence methods, all polyps, including those with the potential for malignancy, can be removed, thereby reducing interval colorectal cancer rates. As such, the removal of all polyps may become the best marker of endoscopy quality. Thus, we present a viewpoint on integrating the computer-aided detection (CADe) of polyps with high-accuracy, real-time colonoscopy to challenge quality improvements in the performance of colonoscopy. Colonoscopy for bowel cancer screening involving the integration of a deep learning methodology (ie, integrating artificial intelligence with CADe systems) has been assessed in an effort to increase the adenoma detection rate. In this viewpoint, a few studies are described, and their results show that CADe systems are able to increase screening sensitivity. The detection of adenomatous polyps, which are associated with a potential risk of progression to colorectal cancer, and their removal are expected to reduce cancer incidence and mortality rates. However, so far, artificial intelligence methods do not increase the detection of cancer or large adenomatous polyps but contribute to the detection of small precancerous polyps.
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