[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"anterior-segment-diseases\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:anterior-segment-diseases":25},{"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":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":16,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":23,"conditions":24,"keywords":26,"overallStatus":28,"whyStopped":4,"lastUpdateSubmitDate":29,"lastUpdatePostDateStruct":30,"startDateStruct":33,"completionDateStruct":35,"leadSponsor":37,"locationsCount":5},"100627387","multimodal-deep-learning-model-for-multi-task-diagnosis-and-triage-suggestions-of-ophthalmic-diseases-100627387",false,"NCT07447973","Multimodal Deep Learning Model for Multi-task Diagnosis and Triage Suggestions of Ophthalmic Diseases","Development and Validation of Multimodal Deep Learning Model for Autonomous Diagnosis, Generative Reporting, and Specialist Referral in Ophthalmic Diseases: An International Multicenter Cohort Study","Inclusion Criteria:\n\n1. Informed consent obtained;\n2. Participants should be sufficiently able to read, write, and understand Chinese or English;\n3. For normal participants: individuals should have no concerns related to their eyes.\n4. For participants with eye-related chief complaints: individuals should have specific concerns or issues related to their eyes.\n\nExclusion Criteria:\n\n1. Incomplete clinical data to support final diagnosis;\n2. Patients who, in the opinion of the attending physician or clinical study staff, are too medically unstable to participate in the study safely.",true,"ALL","18 Years",{"count":20,"type":21},2000,"ESTIMATED","OBSERVATIONAL","Accurate and comprehensive interpretation of anterior segment diseases from slit-lamp and smartphone photographs remains a clinical challenge due to the limited specificity and structure of existing Artificial Intelligence tools. The purpose of this international, multicenter clinical trial is to developed and validated an agent-based framework that integrates vision-language models and large language models to enhance the diagnostic workflow of anterior segment diseases.",[25],"Anterior Segment Diseases",[25,27],"Artificial Intelligence","RECRUITING","2026-03-03",{"date":31,"type":32},"2026-03-05","ACTUAL",{"date":34,"type":32},"2025-07-28",{"date":36,"type":21},"2027-12-31",{"name":38,"class":39},"Guangdong Provincial People's Hospital","OTHER"]