Computer-Assisted Detection (CADe) of colonic polyps during colonoscopy reduces cancer-related healthcare costs – a single Singaporean institution cost effectiveness analysis

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Background and aims: Colonoscopy remains as an invaluable diagnostic and therapeutic tool used for the screening and evaluation of colonic pathologies. Periodic endoscopic screening allowing for early detection and removal of pre-malignant lesions, resulting in significant overall decreased incidence and mortality from colorectal cancer. Despite its added costs, artificial intelligence (AI) has been increasingly utilized in the field of endoscopy, boasting benefits of improving adenoma detection rates while reducing endoscopists’ mental fatigue intra-procedurally. As such, further evaluation of the financial impact of such AI-usage in screening colonoscopies was performed to assess if routine use of AI-guidance in screening colonoscopies was justifiable. Methods: This study conducted a cost effectiveness analysis of the GI Genius™ CADx Intelligent Endoscopy Module (US-DG-2000309, 2021 Medtronic) system in our local Singaporean tertiary public healthcare institution. A decision tree model was used to calculate Incremental Cost Effectiveness Ratio (ICER), comparing cancer-related costs for patients who underwent conventional colonoscopy versus colonoscopy with AI guidance. Results: With a calculated ICER value of 0.72, our study suggests that AI-assisted colonoscopy was indeed cost effective. Quality-Adjusted Life Years (QALY) values derived for each subgroup of patients further demonstrated overall improved quality of life reported by patients who underwent AI-assisted colonoscopy, as compared to conventional colonoscopy. Conclusion: Our study supports the incorporation of AI guidance into routine colonoscopic evaluation in view of increased cost effectiveness and improved overall quality of life achieved. However, further studies and analysis must be undertaken before reliably determining the cost-effectiveness of AI on regional or international scales.

This publication has 20 references indexed in Scilit: