Evaluation of Artificial Intelligence: Computer-aided Detection of Colorectal Polyps

Abstract

Objectives: Colonoscopy is the gold standard for screening cancer and precancerous lesions in the large intestine. Recently, remarkable advances in artificial intelligence (AI) have led to the development of various computer-aided detection (CADe) systems for colonoscopy. This study aimed to evaluate the usefulness of AI for colonoscopy using CAD-EYEⓇ (Fujifilm, Tokyo, Japan) to calculate the adenoma miss rate (AMR). Methods: This randomized, open-label, single-center, tandem study was conducted at Fukuoka University Chikushi Hospital from February 2022 to November 2022. Patients were randomly assigned to the CADe or non-CADe group. Immediately after the completion of the first endoscopy by an endoscopist, a new endoscopist was assigned to perform the second endoscopy. As a result, different endoscopists performed the examinations in a tandem fashion. A missed lesion was defined as a newly detected colorectal polyp by the second endoscopy. Finally, the AMR was compared between the two groups. Results: The study population comprised 48 patients in the CADe group and 46 patients in the non-CADe group. The AMR was 17.4% in the CADe group and 30.3% in the non-CADe group. Therefore, the AMR in the CADe group was statistically significantly lower than that in the non-CADe group (P=0.009). Conclusions: The application of CAD-EYEⓇ to colonoscopy reduced the AMR. Overall, CAD-EYEⓇ might be useful for reducing missed colorectal adenomas.

Keywords

• artificial intelligence
• computer-aided detection
• adenoma miss rate
• adenoma detection rate