Ignite Creation Date:
2025-12-25 @ 2:10 AM
Ignite Modification Date:
2025-12-26 @ 12:37 AM
Study NCT ID:
NCT07291960
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-12-18
First Post:
2025-12-05
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Retinal Clinical Assessment With AI-derived Quantitative Information
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003930', 'term': 'Diabetic Retinopathy'}, {'id': 'D047728', 'term': 'Myopia, Degenerative'}, {'id': 'D012167', 'term': 'Retinal Perforations'}, {'id': 'D019773', 'term': 'Epiretinal Membrane'}, {'id': 'D012170', 'term': 'Retinal Vein Occlusion'}], 'ancestors': [{'id': 'D012164', 'term': 'Retinal Diseases'}, {'id': 'D005128', 'term': 'Eye Diseases'}, {'id': 'D003925', 'term': 'Diabetic Angiopathies'}, {'id': 'D014652', 'term': 'Vascular Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}, {'id': 'D048909', 'term': 'Diabetes Complications'}, {'id': 'D003920', 'term': 'Diabetes Mellitus'}, {'id': 'D004700', 'term': 'Endocrine System Diseases'}, {'id': 'D009216', 'term': 'Myopia'}, {'id': 'D012030', 'term': 'Refractive Errors'}, {'id': 'D020246', 'term': 'Venous Thrombosis'}, {'id': 'D013927', 'term': 'Thrombosis'}, {'id': 'D016769', 'term': 'Embolism and Thrombosis'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 21}, 'targetDuration': '21 Days', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-12-15', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-11', 'completionDateStruct': {'date': '2026-01-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-12-05', 'studyFirstSubmitDate': '2025-12-05', 'studyFirstSubmitQcDate': '2025-12-05', 'lastUpdatePostDateStruct': {'date': '2025-12-18', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-12-18', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2026-01-15', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Expert-rated clinical report quality', 'timeFrame': 'Assessed after completion of all reporting tasks (approximately 1-2 weeks per participant)', 'description': 'All clinical reports generated by clinicians in both the AI-assisted and control groups will be anonymized and independently evaluated by a separate panel of senior ophthalmologists who are blinded to group allocation. The expert evaluators will score each report using predefined criteria assessing accuracy, completeness, clarity, consistency with the fundus image, and overall clinical quality. Scores will be recorded using a standardized multi-dimensional rating scale. The primary outcome is the mean overall quality score per report.'}]}, 'conditionsModule': {'conditions': ['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)']}, 'descriptionModule': {'briefSummary': "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."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'The study population consists of practicing ophthalmologists and ophthalmology trainees who are responsible for interpreting fundus images and generating clinical reports. These clinicians will be randomly assigned to either the intervention group, which has access to AI-derived quantitative retinal information during report writing, or the control group, which performs report writing using only the original fundus images without AI assistance.\n\nA separate panel of senior ophthalmologists, who are not involved in the reporting task, will serve as blinded expert evaluators. They will independently assess all completed reports based on predefined quality dimensions, including accuracy, completeness, clarity, and consistency of interpretation.\n\nThe retinal fundus images used in this study are de-identified clinical images representing a range of normal and abnormal retinal presentations. All images are of sufficient quality for interpretation and contain no patient-identifiable information', 'healthyVolunteers': True, 'eligibilityCriteria': '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).'}, 'identificationModule': {'nctId': 'NCT07291960', 'briefTitle': 'Retinal Clinical Assessment With AI-derived Quantitative Information', 'organization': {'class': 'OTHER', 'fullName': 'Beijing Tongren Hospital'}, 'officialTitle': 'AI-derived Retinal Quantification Versus Routine Clinical Interpretation in Ophthalmic Assessment: a Randomized Controlled Trial', 'orgStudyIdInfo': {'id': 'TRECK2018-056-GZ(2022)-07'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'AI-derived retinal quantification', 'interventionNames': ['Diagnostic Test: AI-derived retinal quantitative information-assisted reporting']}, {'label': 'Routine clinical interpretation'}, {'label': 'Outcome Assessor'}], 'interventions': [{'name': 'AI-derived retinal quantitative information-assisted reporting', 'type': 'DIAGNOSTIC_TEST', 'description': "Clinicians assigned to the intervention arm will complete retinal clinical reports with access to an AI system that provides automated retinal feature quantification. The system generates multiple quantitative retinal biomarkers-including vessel characteristics, optic nerve head metrics, macular indices, and other region-specific structural measurements-derived from automated segmentation of each fundus image.\n\nDuring report writing, clinicians can view these AI-generated quantitative values alongside the image. The system does not provide diagnostic labels, impressions, or textual interpretations; it only supplies numerical measurements intended to support clinicians' assessment. All clinical judgments, narrative descriptions, and final conclusions in the report are made solely by the clinician.", 'armGroupLabels': ['AI-derived retinal quantification']}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Beijing Tongren Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}