Since colonoscopy and polypectomy were introduced, Colorectal Cancer (CRC) incidence and mortality decreased significantly. Although we have entered the era of quality measurement and improvement, literature shows that a considerable amount of colorectal neoplasia is still missed by colonoscopists up to 25%, leading to an high rate of interval colorectal cancer that account for nearly 10% of all diagnosed CRC. Two main reasons have been recognised: recognition failure and mucosal exposure. For this purpose, Artificial Intelligence (AI) systems have been recently developed that identify a "hot" area during the endoscopic examination. In retrospective studies, where the systems are tested with a batch of unknown images, deep learning systems have shown very good performances, with high levels of accuracy. Of course, this setting may not reflect actual clinical practice where different pitfalls can occur, like suboptimal bowel preparation or poor examination technique. For this reason, a number of randomised clinical trials have recently been published where AI was tested in real time during endoscopic examinations. We present here an overview on recent literature addressing the performance of Computer Assisted Detection (CADe) of colorectal polyps in colonoscopy.
Keywords: Adenoma detection rate; Artificial intelligence; CADe system; Colonoscopy.
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