Diabetic Retinopathy
Conditions
Brief summary
To validate the artificial intelligence subsystem of the DDART medical device for the automated detection of lesions compatible with diabetic retinopathy in a random sample of retinal fundus photographs. Secondary Objectives To determine the sensitivity and specificity of the artificial intelligence subsystem for the detection of diabetic retinopathy. To estimate the overall diagnostic accuracy and the area under the receiver operating characteristic (ROC) curve (AUC). To compare the performance of the algorithm with that of experienced ophthalmologists. To evaluate the ability of the model to distinguish between different stages of disease severity
Interventions
DIAGNOSTIC_TESTColor retinal fundus photograph
Diagnostic Test: color fundus photograph Description: Color retinal fundus photographs will be acquired from: Digital non-mydriatic fundus cameras.
Sponsors
Democritus University of Thrace
Study design
Observational model
COHORT
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
Age ≥18 years Availability of a high-resolution color fundus photograph Confirmed diagnosis established by an ophthalmology specialist
Exclusion criteria
Poor-quality retinal images Concomitant ocular diseases that interfere with image interpretation
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Validation the artificial intelligence subsystem of the DDART medical device for the automated detection of lesions compatible with diabetic retinopathy in a random sample of retinal fundus photographs. | 1 year |
Countries
Greece
Contacts
CONTACTGeorgios Labiris
Outcome results
None listed