Diabetic Retinopathy
Conditions
Keywords
Diabetic Retinopathy, Screening, Deep Learning Algorithm, Human Grader
Brief summary
Efficiency and effectiveness of real-world diabetic retinopathy screening by artificial intelligent (AI) are limited. Investigators will implement AI for diabetic retinopathy screening in 13 health districts in Thailand and investigate the efficiency, effectiveness as well as patients and health care personnel's satisfaction by an implementation research.
Interventions
DIAGNOSTIC_TESTDiabetic retinopathy screening by artificial intelligence
Screening diabetic patients' eyes with AI through digital health platform
DIAGNOSTIC_TESTDiabetic retinopathy screening by healthcare personnel
Screening diabetic patients' eyes by conventional method (healthcare personnel)
Sponsors
Health Systems Research Institute,Thailand
Department of Medical Services Ministry of Public Health of Thailand
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Type 1 or 2 diabetes mellitus patients whose name are in primary hospital record 2. No full-time ophthalmologists in those primary hospital 3. Age more than or equal to 18 years 4. Eligible for fundus photo imaging at least 1 eye
Exclusion criteria
1. Type 1 or 2 diabetes mellitus patients whose name are in primary hospital record that have full-time ophthalmologists 2. Patients who previously diagnosed with other causes of macular edema, for example, Age-related Macular Degeneration, Radiation Retinopathy, Retinal Vein Occlusion etc. 3. History of retinal laser or surgery 4. Other ocular diseases that require referral to ophthalmologists 5. Not eligible for fundus photo imaging for both eyes (any causes)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Effectiveness of AI in diabetic retinopathy screening | Throughout the whole period of screening, approximately 6 months | Referral adherance of patients in AI group in percentage |
| Efficiency of AI in diabetic retinopathy screening | Throughout the whole period of screening, approximately 6 months | Down time and failure rate of AI system |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Satisfaction of patients and health care personnel in AI-based screening | At the end of the screening, approximately at Month 6 | Measurement of health care personnel's satisfaction by well-developed questionnaire |
Contacts
Primary ContactPaisan Ruamviboonsuk, Dr.
Backup ContactMethaphon Chainakul, Dr.
Outcome results
None listed