Artificial intelligence for non‐polypoid colorectal neoplasms. Issue 2 (7th October 2020)

• Record Type: Journal Article
• Authors: Hassan, Cesare; Bhandari, Pradeep; Antonelli, Giulio; Repici, Alessandro
• Is Part Of: Digestive endoscopy. Volume 33:Issue 2(2021)
• Page Start: 285
• Page End: 289
• Publication Date: 2020-10-07
• Subjects: artificial intelligence -- CADe -- colonoscopy -- detection -- non‐polypoid neoplasia; Digestive organs -- Diseases -- Periodicals; Digestive organs -- Diseases -- Diagnosis -- Periodicals; Endoscopy -- Periodicals; Digestive System Diseases -- diagnosis -- Periodicals; Digestive System Diseases -- therapy -- Periodicals; Endoscopy -- Periodicals; 616.3
• DOI: 10.1111/den.13807
• Languages: English
• ISSNs: 0915-5635

Abstract

Abstract : The miss rate of flat advanced colorectal neoplasia is still unacceptably high, especially in the Western setting, notwithstanding the widespread implementation of quality improvement programs and training. It is well known that flat morphology is associated with miss rate of colorectal neoplasia, and that this subset of lesions often shows a more aggressive biological behaviour. Artificial intelligence (AI) applied to the detection of colorectal neoplasia has been shown to increase adenoma detection rate, consistently across all lesion sizes and locations in the colon. However, there is still uncertainty whether AI can reduce the miss rate of flat advanced neoplasia, mainly because all published trials report a low number of flat colorectal lesions in their training sets, and this could reduce AI accuracy for this subset of lesions. In addition, flat lesions have different morphologies with variable prevalence and potentially different accuracy in their detection. For example, the subtle appearance and rarer frequency of a non‐granular laterally spreading tumor (LST) could be much harder to identify than a granular mixed LST. In this review, we present a summary of the evidence on the role of AI in the identification of colorectal flat neoplasia, with a focus on data regarding presence of LSTs in the training/validation sets of the AI systems currently available on the market.