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Cost savings in colonoscopy with artificial intelligence-aided polyp diagnosis: an add-on analysis of a clinical trial (with video)

Published:March 30, 2020DOI:https://doi.org/10.1016/j.gie.2020.03.3759

      Background and Aims

      Artificial intelligence (AI) is being implemented in colonoscopy practice, but no study has investigated whether AI is cost saving. We aimed to quantify the cost reduction using AI as an aid in the optical diagnosis of colorectal polyps.

      Methods

      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 polyps in the 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 the number and reimbursement of colonoscopies conducted under public health insurances in 4 countries.

      Results

      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 and 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 US$149.2 million in Japan, 6.9% and US$12.3 million in England, 7.6% and US$1.1 million in Norway, and 10.9% and US$85.2 million in the United States, respectively, compared with the resect-all-polyps strategy.

      Conclusions

      The use of AI to enable the diagnose-and-leave strategy results in substantial cost reductions for colonoscopy.

      Abbreviations:

      AI (artificial intelligence), CI (confidence interval), IQR (interquartile range), NPV (negative predictive value), PIVI (Preservation and Incorporation of Valuable Endoscopic Innovation)
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      Linked Article

      • Artificial intelligence for polyp characterization: Don’t save on your competence!
        Gastrointestinal EndoscopyVol. 92Issue 4
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          Colonoscopy screening comes with a significant cost. If you sum up the costs due to health care personnel, endoscopy technology, facilities, and histopathology, colonoscopy is among the most expensive diagnostic procedures, with estimates comparable with whole-body CT or magnetic resonance imaging. When these costs are projected at population level, the absolute magnitude is exceptional, inasmuch as it is estimated to correspond to an annual gross expenditure of more than U.S. $775 million.1
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