[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"stye\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:stye":31},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":13,"acronym":14,"eligibilityCriteria":15,"healthyVolunteers":16,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":23,"conditions":24,"keywords":41,"overallStatus":46,"whyStopped":4,"lastUpdateSubmitDate":47,"lastUpdatePostDateStruct":48,"startDateStruct":51,"completionDateStruct":53,"leadSponsor":55,"locationsCount":5},"100643686","development-of-a-mobile-terminal-based-intelligent-detection-system-for-multiple-anterior-segment-diseases-of-the-eye-100643686",false,"NCT07634913","Development of a Mobile Terminal-Based Intelligent Detection System for Multiple Anterior Segment Diseases of the Eye","LENS","Inclusion Criteria:\n\n* Adults aged 18 years or older;\n* Willing to participate and able to provide written informed consent prior to enrollment.\n\nExclusion Criteria:\n\n* Unable to cooperate with anterior segment image capture (including smartphone-based photography or slit-lamp biomicroscopy).",true,"ALL","18 Years",{"count":20,"type":21},3000,"ESTIMATED","OBSERVATIONAL","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.\n\nThe 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.\n\nThe 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.\n\nThe 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.",[25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40],"Artifical Intelligence","Cataract","Pterygium","Keratopathy","Subconjunctival Hemorrhage","Conjunctivitis","Stye","Blepharitis","Entropion","Ectropion","Exophthalmos","Irregular Pupils","Conjunctival Concretions","Hyphema","Hypopyon","Corneal Transplant Status",[42,43,44,45],"Artificial Intelligence","Standalone Deployment","Smartphone","Eye Disease Screening","RECRUITING","2026-06-08",{"date":49,"type":50},"2026-06-09","ACTUAL",{"date":52,"type":50},"2023-12-12",{"date":54,"type":21},"2028-12",{"name":56,"class":57},"Zhongshan Ophthalmic Center, Sun Yat-sen University","OTHER"]