[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"pathological-myopia\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:pathological-myopia":29},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,2,0,[8,45],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":16,"sex":17,"minAge":4,"maxAge":4,"enrollmentInfo":18,"targetDuration":21,"studyType":22,"phases":4,"briefSummary":23,"conditions":24,"keywords":4,"overallStatus":33,"whyStopped":4,"lastUpdateSubmitDate":34,"lastUpdatePostDateStruct":35,"startDateStruct":38,"completionDateStruct":40,"leadSponsor":42,"locationsCount":4},"100615388","retinal-clinical-assessment-with-ai-derived-quantitative-information-100615388",false,"NCT07291960","Retinal Clinical Assessment With AI-derived Quantitative Information","AI-derived Retinal Quantification Versus Routine Clinical Interpretation in Ophthalmic Assessment: a Randomized Controlled Trial","Inclusion Criteria:\n\nClinician Participants (Report Writers)\n\n1. Board-certified ophthalmologists or ophthalmology trainees (registrars or fellows) with clinical experience in interpreting fundus images.\n2. Capable of independently completing retinal clinical reports based on fundus photography.\n3. Willing and able to participate in the study tasks (report writing) under assigned study conditions.\n4. Able to provide informed consent.\n\nExpert Evaluators (Outcome Assessors)\n\n1. Senior ophthalmologists with at least 5 years of post-certification clinical experience.\n2. Not involved in the report-writing stage of the study.\n3. Willing to evaluate de-identified reports across predefined quality dimensions.\n4. Able to provide informed consent.\n\nFundus Images (Data Inputs)\n\n1. Retinal fundus photographs of sufficient quality for clinical interpretation.\n2. Images representing a range of common retinal findings (normal or abnormal).\n3. Previously collected, de-identified images with no patient-identifiable information.\n\nExclusion Criteria:\n\nClinician Participants\n\n1. Lack of experience in interpreting fundus images (e.g., interns, medical students).\n2. Prior involvement in the development, training, or validation of the AI system being tested.\n3. Inability to complete reporting tasks due to time constraints or technical limitations.\n4. Any condition that may interfere with ability to perform study tasks (e.g., prolonged absence).\n\nExpert Evaluators\n\n1. Participation in the intervention or control reporting arms.\n2. Prior exposure to or involvement in development of the AI system.\n3. Any conflict of interest affecting impartiality of report quality evaluation.\n\nFundus Images\n\n1. Poor-quality images with insufficient clarity for interpretation.\n2. Images containing artifacts or cropping that prevent accurate segmentation or assessment.\n3. Images with any remaining patient identifiers (excluded to maintain confidentiality).",true,"ALL",{"count":19,"type":20},29,"ESTIMATED","21 Days","OBSERVATIONAL","This randomized controlled trial evaluates whether providing clinicians with AI-derived quantitative retinal information improves the quality and efficiency of retinal clinical assessment. Participating ophthalmologists and ophthalmology trainees will be randomly assigned to one of two groups. The intervention group will write clinical reports with access to automated quantitative measurements generated from fundus image analysis, including multiple retinal structural and vascular biomarkers. The control group will complete the same reporting tasks using only the original fundus images without AI-generated quantitative information.\n\nAll reports produced by both groups will be de-identified and independently evaluated by a separate panel of senior ophthalmologists who are blinded to group allocation. The expert evaluators will assess report accuracy, completeness, clarity, and overall clinical quality using predefined scoring criteria. The study aims to determine whether access to quantitative retinal biomarkers enhances clinicians' reporting performance and reduces reporting time during retinal assessment tasks.",[25,26,27,28,29,30,31,32],"no Obvious Abnormalities","Diabetic Retinopathy (DR)","AMD","Cup-to-disc Ratio Bigger Than 0.5","Pathological Myopia","Macular Hole","Epiretinal Membrane","Retinal Vein Occlusion (RVO)","NOT_YET_RECRUITING","2026-04-28",{"date":36,"type":37},"2026-04-29","ACTUAL",{"date":39,"type":20},"2026-04-15",{"date":41,"type":20},"2026-05-15",{"name":43,"class":44},"Beijing Tongren Hospital","OTHER",{"id":46,"slug":47,"hasResults":11,"nctId":48,"briefTitle":49,"officialTitle":50,"acronym":4,"eligibilityCriteria":51,"healthyVolunteers":16,"sex":17,"minAge":4,"maxAge":4,"enrollmentInfo":52,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":54,"conditions":55,"keywords":4,"overallStatus":33,"whyStopped":4,"lastUpdateSubmitDate":61,"lastUpdatePostDateStruct":62,"startDateStruct":64,"completionDateStruct":66,"leadSponsor":68,"locationsCount":70},"100586642","an-interpretable-fundus-diseases-report-generating-system-based-on-weakly-labelings-100586642","NCT06918028","An Interpretable Fundus Diseases Report Generating System Based On Weakly Labelings","To Construct an Interpretable Multi-modal Report Generating System For Fundus Diseases Based On Weakly Labelings","Inclusion Criteria:\n\n* Disease group: All multimodal fundus examination images containing fundus lesions, examined from January 2011 to December 2023, including fundus photography, as well as OCT, OCTA, FFA, ICGA, B-ultrasound and corresponding imaging reports. Images could be either clear or unclear, reports are either complete or incomplete.\n* Normal group: All multimodal fundus examination images without fundus lesions, examined from January 2011 to December 2023, including fundus photography, as well as OCT, OCTA, FFA, ICGA, B-ultrasound and corresponding imaging reports. Images could be either clear or unclear, reports are either complete or incomplete.\n\nExclusion Criteria:\n\n* Disease group: 1. The image has serious quality problems; 2. The diagnostic report lacks key information.\n* Normal group: 1. The image has serious quality problems; 2. The diagnostic report lacks key information.",{"count":53,"type":20},9999,"To establish a multimodal fundus image report generation model to realize an interpretable system for multiple fundus diseases, multimodal image analysis, diagnosis, and treatment decision automatic reporting based on weakly labeled training data. Construct an interpretable feature fusion network for the clinical and imaging features of fundus lesions, and we hope to extract new imaging markers that can predict the occurrence and progression of various fundus lesions at an early stage, and ultimately verify them in real clinical data, further providing possible directions for exploring the molecular mechanisms of refractory fundus lesions, and may also provide new ideas for the precise prevention and treatment of fundus lesions.",[56,57,29,58,59,60],"Retinal Diseases","Choroidal Disease","Diabetic Retinopathy","Age Related Macular Degeneration","Choroidal Neovascularization","2025-04-05",{"date":63,"type":37},"2025-04-09",{"date":65,"type":20},"2025-05",{"date":67,"type":20},"2026-12",{"name":69,"class":44},"Zhongshan Ophthalmic Center, Sun Yat-sen University",1]