[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100643686":3},{"organization":4,"armGroups":7,"interventions":18,"overallOfficials":24,"centralContacts":28,"locations":35,"responsibleParty":51,"collaborators":10,"id":53,"slug":54,"hasResults":55,"nctId":56,"briefTitle":57,"officialTitle":57,"acronym":58,"eligibilityCriteria":59,"healthyVolunteers":60,"sex":61,"minAge":62,"maxAge":10,"enrollmentInfo":63,"targetDuration":10,"studyType":66,"phases":10,"briefSummary":67,"conditions":68,"keywords":85,"overallStatus":37,"whyStopped":10,"lastUpdateSubmitDate":90,"lastUpdatePostDateStruct":91,"startDateStruct":94,"completionDateStruct":96,"leadSponsor":98,"locationsCount":99},{"fullName":5,"class":6},"Zhongshan Ophthalmic Center, Sun Yat-sen University","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"On-Device Deployment Group",null,"Participants screened using a smartphone-based AI system that performs all image inference locally on the device without requiring internet connectivity. The AI system analyzes smartphone-captured anterior segment images and generates diagnostic outputs entirely on the smartphone hardware, independent of cloud infrastructure or network access.",[13],"Device: Smartphone-based on-device artificial intelligence system for anterior segment eye disease screening",{"label":15,"type":10,"description":16,"interventionNames":17},"Cloud-Based Deployment Group","Participants screened using a functionally equivalent deployment of the same AI model architecture, in which smartphone-captured anterior segment images are transmitted to a remote server for inference, with diagnostic outputs returned via internet connection. This group serves as the comparator to evaluate the operational differences attributable solely to deployment modality, as the underlying model architecture is identical between the two groups.",[13],[19],{"type":20,"name":21,"description":22,"armGroupLabels":23,"otherNames":10},"DEVICE","Smartphone-based on-device artificial intelligence system for anterior segment eye disease screening","A structured-pruned one-stage object-detection model deployed as a standalone Android application, performing all image inference on-device without internet connectivity, designed to detect 16 anterior segment eye diseases from smartphone-captured images.",[15,9],[25],{"name":26,"affiliation":5,"role":27},"Longhui Li","PRINCIPAL_INVESTIGATOR",[29,34],{"name":30,"role":31,"phone":32,"phoneExt":10,"email":33},"Haotian Lin","CONTACT","+86 13802793086","haot.lin@hotmail.com",{"name":26,"role":31,"phone":10,"phoneExt":10,"email":10},[36],{"facility":5,"status":37,"city":38,"state":39,"zip":40,"country":41,"countryCode":42,"cosmosGeoPoint":43,"geoPoint":48,"contacts":49},"RECRUITING","Guangzhou","Guangdong","510060","China","CN",{"type":44,"coordinates":45},"Point",[46,47],113.25,23.11667,{"lat":47,"lon":46},[50],{"name":30,"role":31,"phone":32,"phoneExt":10,"email":33},{"type":52,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR","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":64,"type":65},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.",[69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84],"Artifical Intelligence","Cataract","Pterygium","Keratopathy","Subconjunctival Hemorrhage","Conjunctivitis","Stye","Blepharitis","Entropion","Ectropion","Exophthalmos","Irregular Pupils","Conjunctival Concretions","Hyphema","Hypopyon","Corneal Transplant Status",[86,87,88,89],"Artificial Intelligence","Standalone Deployment","Smartphone","Eye Disease Screening","2026-06-08",{"date":92,"type":93},"2026-06-09","ACTUAL",{"date":95,"type":93},"2023-12-12",{"date":97,"type":65},"2028-12",{"name":5,"class":6},1]