Retinal Clinical Assessment With AI-derived Quantitative Information

N/ANot Yet RecruitingNCT07291960

Beijing Tongren Hospital21 enrolled

Overview

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. All 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.

Study Type

OBSERVATIONAL

Enrollment

Conditions

no Obvious Abnormalities Diabetic Retinopathy (DR)AMD Cup-to-disc Ratio Bigger Than 0.5 Pathological Myopia

Interventions

AI-derived retinal quantitative information-assisted reportingDIAGNOSTIC_TEST

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. During 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.

Eligibility

Sex: ALLHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria: Clinician Participants (Report Writers) 1. Board-certified ophthalmologists or ophthalmology trainees (registrars or fellows) with clinical experience in interpreting fundus images. 2. Capable of independently completing retinal clinical reports based on fundus photography. 3. Willing and able to participate in the study tasks (report writing) under assigned study conditions. 4. Able to provide informed consent. Expert Evaluators (Outcome Assessors) 1. Senior ophthalmologists with at least 5 years of post-certification clinical experience. 2. Not involved in the report-writing stage of the study. 3. Willing to evaluate de-identified reports across predefined quality dimensions. 4. Able to provide informed consent. Fundus Images (Data Inputs) 1. Retinal fundus photographs of sufficient quality for clinical interpretation. 2. Images representing a range of common retinal findings (normal or abnormal). 3. Previously collected, de-identified images with no patient-identifiable information. Exclusion Criteria: Clinician Participants 1. Lack of experience in interpreting fundus images (e.g., interns, medical students). 2. Prior involvement in the development, training, or validation of the AI system being tested. 3. Inability to complete reporting tasks due to time constraints or technical limitations. 4. Any condition that may interfere with ability to perform study tasks (e.g., prolonged absence). Expert Evaluators 1. Participation in the intervention or control reporting arms. 2. Prior exposure to or involvement in development of the AI system. 3. Any conflict of interest affecting impartiality of report quality evaluation. Fundus Images 1. Poor-quality images with insufficient clarity for interpretation. 2. Images containing artifacts or cropping that prevent accurate segmentation or assessment. 3. Images with any remaining patient identifiers (excluded to maintain confidentiality).

Outcomes

Primary Outcomes

Expert-rated clinical report quality

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.

Time frame: Assessed after completion of all reporting tasks (approximately 1-2 weeks per participant)