[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"conjunctival-concretions\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:conjunctival-concretions":57},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":4,"hasResults":10,"nctId":11,"briefTitle":12,"officialTitle":12,"acronym":13,"eligibilityCriteria":14,"healthyVolunteers":15,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":18,"targetDuration":4,"studyType":21,"phases":4,"briefSummary":22,"conditions":23,"keywords":40,"overallStatus":45,"whyStopped":4,"lastUpdateSubmitDate":46,"lastUpdatePostDateStruct":47,"startDateStruct":50,"completionDateStruct":52,"leadSponsor":54,"locationsCount":5},"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":19,"type":20},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.",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"Artifical Intelligence","Cataract","Pterygium","Keratopathy","Subconjunctival Hemorrhage","Conjunctivitis","Stye","Blepharitis","Entropion","Ectropion","Exophthalmos","Irregular Pupils","Conjunctival Concretions","Hyphema","Hypopyon","Corneal Transplant Status",[41,42,43,44],"Artificial Intelligence","Standalone Deployment","Smartphone","Eye Disease Screening","RECRUITING","2026-06-08",{"date":48,"type":49},"2026-06-09","ACTUAL",{"date":51,"type":49},"2023-12-12",{"date":53,"type":20},"2028-12",{"name":55,"class":56},"Zhongshan Ophthalmic Center, Sun Yat-sen University","OTHER",""]