Department of Health Management and Health Economics, Institute of Health and Society, University of Oslo, Oslo, Norway.
Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.
Gastrointest Endosc. 2020 Oct;92(4):905-911.e1. doi: 10.1016/j.gie.2020.03.3759. Epub 2020 Mar 30.
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.
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.
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.
The use of AI to enable the diagnose-and-leave strategy results in substantial cost reductions for colonoscopy.
人工智能（AI）正在被应用于结肠镜检查实践中，但目前尚无研究探讨 AI 是否能够节省成本。本研究旨在量化 AI 在辅助结直肠息肉的光学诊断中降低成本的效果。
本研究是一项临床试验的附加分析，该试验旨在评估 AI 对结直肠息肉（即肿瘤性与非肿瘤性）进行区分的性能。我们将所有直径≤5mm 的直肠乙状结肠息肉患者纳入分析。比较了两种情况下的平均结肠镜检查成本：（1）AI 预测支持的诊断后即走策略（即当预测为非肿瘤性时，不切除微小直肠乙状结肠息肉）；（2）切除所有息肉策略。还根据 4 个国家公共医疗保险下进行的结肠镜检查数量和报销情况，计算了结肠镜检查的年度总费用。
共纳入 207 例 250 枚微小直肠乙状结肠息肉患者（104 枚肿瘤性、144 枚非肿瘤性和 2 枚性质不明）。AI 正确区分了 93.3%的肿瘤性息肉，特异性为 95.2%，阴性预测值为 95.2%。因此，在诊断后即走策略下，105 枚息肉被切除，145 枚息肉被保留，该策略估计可使日本、英格兰、挪威和美国的平均结肠镜检查成本分别降低 18.9%和 149.2 万美元、6.9%和 12.3 万美元、7.6%和 110 万美元、10.9%和 8520 万美元，结肠镜检查年度总报销分别降低 18.9%和 149.2 万美元、6.9%和 12.3 万美元、7.6%和 110 万美元、10.9%和 8520 万美元，与切除所有息肉策略相比。
使用 AI 支持诊断后即走策略可显著降低结肠镜检查的成本。
