Acceptability, applicability, and cost-utility of artificial-intelligence-powered low-cost portable fundus camera for diabetic retinopathy screening in primary health care settings

Abstract

Aims: To evaluate the acceptability, applicability, and cost-utility of AI-powered portable fundus cameras for diabetic retinopathy (DR) screening in Hong Kong, providing a viable alternative screening solution for resource-limited areas. Methods: This pragmatic trial conducted in an optometric clinic and two optical shops. A self-testing system was used, integrating a portable fundus camera and AI software that automatically identified DR. Three months following the screening, selected participants were invited to complete an open-ended questionnaire. Results: A total of 316 subjects participated, with age of 60.80 ± 8.30 years. The success rate of the self-testing system without active assistance was 89 %. Among 61 subjects who completed follow-up interview, a majority agreed that the system and report were easy to follow and understand (85.3 % and 75.4 %). The satisfaction rate was 64 %, and the willingness to use again was 80 %. The AI screening showed a cost saving of 6312.92 USD per QALY, while the adjusted AI model saved 18639. AI screening and adjusted model outperformed traditional screening (Net Monetary Benefit 367,863.31 and 354,904.76 vs 339,919.83 USD). Conclusions: The AI-powered portable fundus camera demonstrated high acceptability and applicability in real-world settings, suggesting that AI screening could be a viable alternative in resource-limited settings.