[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100580254":3},{"organization":4,"armGroups":7,"interventions":18,"overallOfficials":28,"centralContacts":32,"locations":41,"responsibleParty":53,"collaborators":55,"id":86,"slug":87,"hasResults":88,"nctId":89,"briefTitle":90,"officialTitle":90,"acronym":11,"eligibilityCriteria":91,"healthyVolunteers":88,"sex":92,"minAge":93,"maxAge":94,"enrollmentInfo":95,"targetDuration":11,"studyType":98,"phases":99,"briefSummary":101,"conditions":102,"keywords":11,"overallStatus":43,"whyStopped":11,"lastUpdateSubmitDate":104,"lastUpdatePostDateStruct":105,"startDateStruct":108,"completionDateStruct":110,"leadSponsor":112,"locationsCount":113},{"fullName":5,"class":6},"Tsinghua University","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"group A","EXPERIMENTAL",null,[13],"Diagnostic Test: EasyEyeFM-AutoML-AI-platform test",{"label":15,"type":10,"description":11,"interventionNames":16},"group B",[17],"Diagnostic Test: AutoML-EasyEyeFM-AI-platform test",[19,24],{"type":20,"name":21,"description":22,"armGroupLabels":23,"otherNames":11},"DIAGNOSTIC_TEST","EasyEyeFM-AutoML-AI-platform test","AI platforms using",[9],{"type":20,"name":25,"description":26,"armGroupLabels":27,"otherNames":11},"AutoML-EasyEyeFM-AI-platform test","AI-platform using",[15],[29],{"name":30,"affiliation":5,"role":31},"Tien Yin Wong","PRINCIPAL_INVESTIGATOR",[33,38],{"name":34,"role":35,"phone":36,"phoneExt":11,"email":37},"Jinyuan Wang","CONTACT","086-15801191316","jinyuanwang@tsinghua.edu.cn",{"name":39,"role":35,"phone":11,"phoneExt":11,"email":40},"Hongqiu Wang","hwang007@connect.hkust-gz.edu.cn",[42],{"facility":5,"status":43,"city":44,"state":11,"zip":11,"country":45,"countryCode":46,"cosmosGeoPoint":47,"geoPoint":52,"contacts":11},"RECRUITING","Beijing","China","CN",{"type":48,"coordinates":49},"Point",[50,51],116.39723,39.9075,{"lat":51,"lon":50},{"type":31,"investigatorFullName":30,"investigatorTitle":54,"investigatorAffiliation":5,"oldNameTitle":11,"oldOrganization":11},"Professor",[56,58,60,63,65,68,70,72,74,76,78,80,82,84],{"name":57,"class":6},"First Hospital of Tsinghua University",{"name":59,"class":6},"Beijing Tsinghua Changgeng Hospital",{"name":61,"class":62},"Augenarzt-Praxisgemeinschaft Gutblick AG","UNKNOWN",{"name":64,"class":6},"Sankara Nethralaya",{"name":66,"class":67},"Department of Medical Services Ministry of Public Health of Thailand","OTHER_GOV",{"name":69,"class":6},"Moorfields Eye Hospital NHS Foundation Trust",{"name":71,"class":62},"Pomeranian Hospitals",{"name":73,"class":62},"Shanghai Health and Medical Center",{"name":75,"class":6},"Hong Kong Eye Hospital",{"name":77,"class":67},"Royal Victoria Eye and Ear Hospital",{"name":79,"class":6},"Hanyang University Guri Hospital",{"name":81,"class":62},"Alshifa Trust Eye Hospital",{"name":83,"class":6},"Korle-Bu Teaching Hospital, Accra, Ghana",{"name":85,"class":62},"Nippon Medical School Tama Nagayama Hospital","100580254","usability-user-testing-of-the-easy-eyefm-ai-platform-100580254",false,"NCT06834906","Usability User Testing of the Easy-EyeFM AI Platform","Inclusion Criteria:\n\n* Medical students or doctors\n* Have enough time to participate in this program, about 1 hour continuous assessment\n* Agree to sign the informed consent form\n\nExclusion Criteria:\n\n* Poor computer and mobile phone use and reading ability\n* Poor language expression or dialect category is not included in the speech recognition function of the platform","ALL","18 Years","80 Years",{"count":96,"type":97},84,"ESTIMATED","INTERVENTIONAL",[100],"NA","Easy-EyeFM is a code-free artificial intelligence platform designed for ophthalmologists to provide diagnostic and treatment recommendations and help doctors develop customized diagnostic models.\n\nThis project aims to evaluate the usability and user-friendliness of the Easy-EyeFM platform for physicians. The study will gather feedback from medical professionals to evaluate the intuitiveness and effectiveness of the platform in supporting model customization and medical image analysis tasks.\n\nSubjects will participate in the trial of the Easy-EyeFM platform and the comparison with the existing commercial platform (AutoML) at the appointed time. The researchers will inform subjects of relevant precautions and assist them to read the user manual before the test. Subjects will complete model customization and picture diagnosis within the prescribed test time, and according to the test results, Score the human-computer interaction experience of the AI platform, fill in the questionnaire, and finally complete the evaluation content.",[103],"Ocular Diseases","2025-08-14",{"date":106,"type":107},"2025-08-15","ACTUAL",{"date":109,"type":107},"2025-01-26",{"date":111,"type":97},"2025-09",{"name":5,"class":6},1]