Conjunctivitis

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.

Data will be gathered from patients who report having any kind of recurrent conjunctivitis and who, in accordance with clinical practice, are scheduled to have intravitreal injections, glaucoma surgery, cataract surgery, vitrectomy, combined cataract and vitrectomy, or corneal transplantation. The data will be obtained using pseudonyms from the patients' medical records and will include the outcomes of swabs and surgeries performed in accordance with clinical practice for the patient's condition. The following swabs will be taken into consideration for data analysis: Four days before to surgery, a conjunctival swab is used to check for the presence and load of species that do not make up the typical conjunctival flora (Baseline-T0). On the day of operation, a conjunctival swab is taken before to entering the recovery room (T1). Following three minutes of 5% iodopovidone instillation in the eye to be operated on, a conjunctival swab (T2) was used. Additionally, we will gather follow-up data from the medical record 24 and 30 hours after surgery (at day 34 +/- 7 days).