Clinical Research Directory

Telemedicine Follow-up for Routine, Low-Risk Oculoplastic Surgery

The investigators propose utilizing a simple telemedical protocol to allow patients to substitute the first post-operative visit with a remote survey that includes essential post-operative history, vision measurement, and photographs, all of which can be provided using a personal computer, tablet, or smart phone. The investigators have selected for this purpose a subset of oculoplastic procedures involving the eyelid and lacrimal system that have well-reported low rates of serious complications, since high-risk procedures will likely always require close, in-person care. The investigators hypothesize that telemedicine follow-up for the first post-operative week after low-risk oculoplastic surgery will decrease the time burden on patients without compromising their satisfaction or increase the risk of late post-operative complications.

Development of a Mobile Terminal-Based Intelligent Detection System for Multiple Anterior Segment Diseases of the Eye

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 consecutive attempts to characterize the learning curve. Third, a head-to-head field trial directly compares the on-device AI system against a functionally equivalent cloud-based deployment of the same model architecture across key operational dimensions including screening duration, diagnostic performance, and user acceptability. Fourth, population-level screening is conducted among consecutively enrolled community residents at two low-resource sites, with per-disease sensitivity and specificity calculated against reference-standard slit-lamp examinations. Fifth, pre-specified health-economic and environmental analyses compare the two deployment modalities in terms of per-person screening cost, cost-effectiveness, per-inference electricity consumption, and projected carbon emissions at scale. The reference standard for all diagnostic comparisons is slit-lamp biomicroscopic examination performed by board-certified ophthalmologists. The study is designed and reported in accordance with the DECIDE-AI reporting guideline for early-stage clinical evaluation of AI-driven decision-support systems.