Age-Related Macular Degeneration (AMD), Diabetic Retinopathy (DR)
Conditions
Keywords
Artificial Intelligence, imaging analysis, disease diagnosis, macular degeneration, diabetic retinopathy
Brief summary
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.
Detailed description
Artificial Intelligence (AI) has shown significant potential in medical imaging analysis and disease diagnosis, particularly in ophthalmology. Substantial advancements have been made in utilizing AI for diagnosing common ophthalmic diseases, enhancing early detection and improving patient outcomes. Early diagnosis of age-related macular degeneration (AMD) and diabetic retinopathy (DR) is crucial for effective treatment and disease management. However, current clinical diagnoses rely heavily on ophthalmologists, leading to challenges such as low patient attendance rates and unequal distribution of diagnostic resources. To address these issues, this study will provide robust evidence to further validate the diagnostic performance of AI-assisted screening and clinical effectiveness of the VeriSee AI-assisted diagnostic system in the detection of diabetic DR and AMD. VeriSee AMD and VeriSee DR are AI-powered medical software tools designed to screen for AMD and DR, respectively. These systems employ advanced AI algorithms to analyze color fundus photography images, assess disease conditions, and evaluate image quality. By integrating this software into clinical workflows, physicians receive instant diagnostic support, improving efficiency and accessibility in ophthalmic disease screening.
Interventions
OTHERThe VeriSee AI-assisted diagnostic system
VeriSee 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 was conducted because the VeriSee AI-assisted diagnostic system was not used.
Sponsors
National Taiwan University Hospital
Fu Jen Catholic University Hospital
Min-Sheng General Hospital
Ministry of Health and Welfare, Taiwan
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
SCREENING
Masking
NONE
Intervention model description
This trial is expected to use a diagnostic accuracy study to test the effectiveness of the VeriSee system in assisting ophthalmologists in diagnosis, and compare it with the traditional method of ophthalmologists making their own diagnosis through fundus photography to evaluate its sensitivity and specificity.
Eligibility
Sex/Gender
ALL
Age
20 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* 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.
Exclusion criteria
* The patient does not agree to participate in the trial or is unable to provide informed consent.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity | From screening to physician-confirmed diagnosis of AMD or DR, an average of 1 month | 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 | From screening to physician-confirmed diagnosis of AMD or DR, an average of 1 month | 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 | From screening to physician-confirmed diagnosis of AMD or DR, an average of 1 month | 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). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Total Cost Analysis (Including Direct and Indirect Costs) | From enrollment to 12 months after screening | 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. |
Countries
Taiwan
Outcome results
None listed