Cost savings in colonoscopy with artificial intelligence-aided polyp diagnosis: an add-on analysis of a clinical trial (with video).
Mori Y., Kudo S-E., East JE., Rastogi A., Bretthauer M., Misawa M., Sekiguchi M., Matsuda T., Saito Y., Ikematsu H., Hotta K., Ohtsuka K., Kudo T., Mori K.
Artificial intelligence (AI) is being implemented into colonoscopy practice, but no study has investigated whether AI is cost-saving. We quantified the cost reduction from using AI as an aid in the optical diagnosis of colorectal polyps. This study is an add-on analysis of a clinical trial that investigated the performance of AI for differentiating colorectal polyps (ie, neoplastic versus non-neoplastic). We included all patients with diminutive (≤5 mm) rectosigmoid polyp for analyses. The average colonoscopy cost was compared for 2 scenarios: (1) a diagnose-and-leave strategy supported by the AI prediction (ie, diminutive rectosigmoid polyps were not removed when predicted as non-neoplastic), and (2) a resect-all-polyps strategy. Gross annual costs for colonoscopies were also calculated based on numbers and reimbursement of colonoscopies conducted under public health insurances in 4 countries. Overall, 207 patients with 250 diminutive rectosigmoid polyps (104 neoplastic, 144 non-neoplastic, and 2 indeterminate) were included. AI correctly differentiated neoplastic polyps with 93.3% sensitivity, 95.2% specificity, and 95.2% negative predictive value. Thus, 105 polyps were removed whereas 145 were left under the diagnose-and-leave strategy, which was estimated to reduce the average colonoscopy cost and the gross annual reimbursement for colonoscopies by 18.9% and 149.2 million dollars in Japan, 6.9% and 12.3 million dollars in England, 7.6% and 1.1 million dollars in Norway, and 10.9% and 85.2 million dollars in the United States, respectively, compared to the resect-all-polyps strategy. The use of AI to enable the diagnose-and-leave strategy results in substantial cost reductions for colonoscopy.