Impact of Artificial Intelligence on Colonoscopy Surveillance After Polyp Removal: A Pooled Analysis of Randomized Trials
Yuichi Mori, Pu Wang, Magnus Løberg, Masashi Misawa, Alessandro Repici, Marco Spadaccini, Loredana Correale, Giulio Antonelli, Honggang Yu, Dexin Gong, Misaki Ishiyama, Shin-Ei Kudo, Shunsuke Kamba, Kazuki Sumiyama, Yutaka Saito, Haruo Nishino, Peixi Liu, Jeremy R Glissen Brown, Nabil M Mansour, Seth A Gross, Mette Kalager, Michael Bretthauer, Douglas K Rex, Prateek Sharma, Tyler M Berzin, Cesare Hassan, Yuichi Mori, Pu Wang, Magnus Løberg, Masashi Misawa, Alessandro Repici, Marco Spadaccini, Loredana Correale, Giulio Antonelli, Honggang Yu, Dexin Gong, Misaki Ishiyama, Shin-Ei Kudo, Shunsuke Kamba, Kazuki Sumiyama, Yutaka Saito, Haruo Nishino, Peixi Liu, Jeremy R Glissen Brown, Nabil M Mansour, Seth A Gross, Mette Kalager, Michael Bretthauer, Douglas K Rex, Prateek Sharma, Tyler M Berzin, Cesare Hassan
Abstract
Background and aims: Artificial intelligence (AI) tools aimed at improving polyp detection have been shown to increase the adenoma detection rate during colonoscopy. However, it is unknown how increased polyp detection rates by AI affect the burden of patient surveillance after polyp removal.
Methods: We conducted a pooled analysis of 9 randomized controlled trials (5 in China, 2 in Italy, 1 in Japan, and 1 in the United States) comparing colonoscopy with or without AI detection aids. The primary outcome was the proportion of patients recommended to undergo intensive surveillance (ie, 3-year interval). We analyzed intervals for AI and non-AI colonoscopies for the U.S. and European recommendations separately. We estimated proportions by calculating relative risks using the Mantel-Haenszel method.
Results: A total of 5796 patients (51% male, mean 53 years of age) were included; 2894 underwent AI-assisted colonoscopy and 2902 non-AI colonoscopy. When following U.S. guidelines, the proportion of patients recommended intensive surveillance increased from 8.4% (95% CI, 7.4%-9.5%) in the non-AI group to 11.3% (95% CI, 10.2%-12.6%) in the AI group (absolute difference, 2.9% [95% CI, 1.4%-4.4%]; risk ratio, 1.35 [95% CI, 1.16-1.57]). When following European guidelines, it increased from 6.1% (95% CI, 5.3%-7.0%) to 7.4% (95% CI, 6.5%-8.4%) (absolute difference, 1.3% [95% CI, 0.01%-2.6%]; risk ratio, 1.22 [95% CI, 1.01-1.47]).
Conclusions: The use of AI during colonoscopy increased the proportion of patients requiring intensive colonoscopy surveillance by approximately 35% in the United States and 20% in Europe (absolute increases of 2.9% and 1.3%, respectively). While this may contribute to improved cancer prevention, it significantly adds patient burden and healthcare costs.
Keywords: Computer-Aided Diagnosis; Machine Learning; Surveillance Interval.
Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.
Source: PubMed