Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study
- PDF / 11,147,209 Bytes
- 8 Pages / 595.276 x 790.866 pts Page_size
- 91 Downloads / 220 Views
ORIGINAL ARTICLE
Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study Yuchen Luo 1 & Yi Zhang 1 & Ming Liu 1 & Yihong Lai 1 & Panpan Liu 1 & Zhen Wang 1 & Tongyin Xing 1 & Ying Huang 1 & Yue Li 1 & Aiming Li 1 & Yadong Wang 1 & Xiaobei Luo 1 & Side Liu 1 & Zelong Han 1 Received: 20 May 2020 / Accepted: 9 September 2020 # 2020 The Author(s)
Abstract Background and aims Improving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Realtime automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment. Methods The selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists (> 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov. (NCT047126265). Results In this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p < 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p < 0.001), but no difference was found with regard to larger lesions. Conclusions A real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion. Trial Registration clinicaltrials.gov Identifier: NCT047126265 Keywords Colonoscopy . Artificial intelligence . Computer-aided diagnose
Introduction Colorectal cancer (CRC) is the third most commonly diagnosed malignancy and one of the leading causes of cancerrelated death.1 Colonoscopy is the primary method for detecting and removing polyps. The detection and resection of tumor lesions by colonoscopy have been shown to be effective for the prevention of CRC.2 There is evidence that for every 1.0% increase in the rate of adenoma detection, the risk of Yuchen Luo, Yi Zhang and Ming Liu contributed equally to this paper. * Xiaobei Luo [email protected] * Side Liu [email protected] * Zelong Han [email protected] 1
Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
CRC decreases by 3.0%.3 However, colonoscopy is not perfect, and occasionally, interval cancer is detected in patients with a recent normal colonoscopy.4 Due to the characteristics of polyps and operators, polyps are prone to missed diagnosis, and the missed diagnosis rate can be as high as 27%.5,6 Two factors are considered to affect the missed diagnosis rate: blind spots and human error. The first factor can be addressed by using a wide-angle range or wide-angle remote attachm
Data Loading...
