Condition: Artifical Intelligence · Cataract · Pterygium · Sponsor: Zhongshan Ophthalmic Center, Sun Yat-sen University
This is a multi-center, cross-sectional study evaluating a smartphone-based artificial intelligence (AI) system for anterior segment eye disease screening. The system is designed to identify 16 clinically important anterior segment conditions from images captured using a standard Android smartphone. A core design feature of the system is that all image analysis is performed entirely on the smartphone itself, without requiring internet connectivity or cloud-based server infrastructure. The study is motivated by a structural challenge in the deployment of medical AI: systems that depend on cloud infrastructure for inference are non-functional in settings without reliable internet access, which disproportionately excludes populations in low-resource regions where the burden of preventable eye disease is highest. This study evaluates whether an on-device AI system, designed with operational constraints as a primary engineering objective, can deliver clinically acceptable diagnostic performance while remaining operable under real-world connectivity limitations. The study comprises five evaluation components. First, the diagnostic performance of the AI system is benchmarked against board-certified ophthalmologists of varying seniority on a standardized set of smartphone-captured anterior segment images. Second, the usability of the system is evaluated among non-medical users who perform self-administered screening with minimal instruction, with per-screening time recorded across …
This description comes directly from the study's public registry record.
Haotian Lin · +86 13802793086 · haot.lin@hotmail.com
Longhui Li
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| Zhongshan Ophthalmic Center, Sun Yat-sen University | Guangzhou, Guangdong, China | Recruiting |
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Source record: clinicaltrials.gov/study/NCT07634913