Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| Fu Jen Catholic University Hospital | OTHER |
| Min-Sheng General Hospital | OTHER |
| Ministry of Health and Welfare, Taiwan | OTHER_GOV |
Not provided
Not provided
Not provided
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.
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.
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| AI Intervention | Other | Patients will undergo fundus photography screening using artificial intelligence-assisted diagnostic software (VeriSee). Ophthalmologists will independently interpret the same images, and the results will be compared with those generated by the AI. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| The VeriSee AI-assisted diagnostic system | Other | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | From screening to physician-confirmed diagnosis of AMD or DR, an average of 1 month |
| 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. | From screening to physician-confirmed diagnosis of AMD or DR, an average of 1 month |
| 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). | From screening to physician-confirmed diagnosis of AMD or DR, an average of 1 month |
| Measure | Description | Time Frame |
|---|---|---|
| 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. |
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| National Taiwan University Hospital | Recruiting | Taipei | Taiwan | 100225 | Taiwan |
Not provided
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.
Not provided
Not provided
Not provided
Not provided
|
| Data collection from the patient's clinical history | Other | Data collection from the patient's clinical history was conducted because the VeriSee AI-assisted diagnostic system was not used. |
|
| From enrollment to 12 months after screening |
| ID | Term |
|---|---|
| D008268 | Macular Degeneration |
| D003930 | Diabetic Retinopathy |
| ID | Term |
|---|---|
| D012162 | Retinal Degeneration |
| D012164 | Retinal Diseases |
| D005128 | Eye Diseases |
| D003925 | Diabetic Angiopathies |
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |
| D048909 | Diabetes Complications |
| D003920 | Diabetes Mellitus |
| D004700 | Endocrine System Diseases |
Not provided
Not provided