This study aims to evaluate the effectiveness of an artificial intelligence (AI)-assisted screening system in ophthalmic diagnosis. Using AI-based fundus photography, the system will assist physicians in diagnosing three common eye diseases: age-related macular degeneration and diabetic retinopathy (DR). The AI system will analyze fundus images from participants and rapidly generate detection results for ophthalmologists' reference in making final diagnoses and clinical decisions. The study will assess the clinical benefits of the AI-assisted diagnostic system, providing scientific evidence to enhance the efficiency of ophthalmic disease diagnosis and treatment.
VeriSee AMD is used in non-retinal subspecialty ophthalmology clinics for adults aged 50 and above.
VeriSee DR is used in non-retinal subspecialty clinics for diabetic patients aged 20 and above.
The patient does not agree to participate in the trial or is unable to provide informed consent.
Single group
The VeriSee AI-assisted diagnostic system
Other interventionVeriSee AMD, VeriSee DR, and VeriSee GLC are AI-based medical software devices designed for screening age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma, respectively. These systems utilize advanced AI algorithms to analyze color fundus photography images for disease assessment. By installing the software on a computer, the system can evaluate image quality, predict disease conditions, and instantly provide results to clinical physicians, serving as a diagnostic aid.
Data collection from the patient's clinical history
Other interventionData collection from the patient's clinical history was conducted because the VeriSee AI-assisted diagnostic system was not used.
Sensitivity
The sensitivity of the index test (VeriSee) was calculated as the proportion of participants with reference standard-confirmed disease who were correctly identified as positive by the AI-assisted diagnostic software.
Specificity
The specificity of the index test was calculated as the proportion of participants without the target condition, as determined by the reference standard, who were correctly classified as negative by the AI-assisted diagnostic tool.
Concordance
Concordance between the AI-assisted diagnosis and the ophthalmologists' interpretation was assessed using the overall agreement rate (i.e., the percentage of cases with identical classification results).
Total Cost Analysis (Including Direct and Indirect Costs)
This measure includes direct medical costs (e.g., screening, follow-up, medication, and treatment), healthcare-related indirect medical costs (e.g., IT system maintenance, healthcare personnel), and non-medical indirect costs (e.g., transportation and productivity loss due to blindness). Costs will be analyzed from both the National Health Insurance perspective and the broader societal perspective.
Click on the lead sponsor to view all of their trials.
National Taiwan University Hospital
Lead sponsor
Fu Jen Catholic University Hospital
Collaborator
Min-Sheng General Hospital
Collaborator
Ministry of Health and Welfare, Taiwan
Collaborator
