To examine the potential for the detection of diabetic retinopathy (DR) using the artificial intelligence (AI)-based software platform Retina-AI.

Operator took fundus images with a non-mydriatic fundus camera as per the Retina-AI CheckEye imaging protocol (an optic disc centered image and a fovea centered image for each eye).Thereafter, operator uploaded fundus images in the AI system for processing by the neural network.

Inclusion Criteria:

1. Documented diagnosis of diabetes mellitus by definition.
2. Understanding of the Study and willingness and ability to sign informed consent
3. Patient age 18 or above
4. Diagnostic for diabetes: 4a) Type 1 diabetes of a lest 5 years of evolution; or 4b) Type 2 diabetes

Exclusion Criteria:

-1. Patients under 18 years of age; 2. Failure to give informed consent; 3. Presence of retinal diseases - acquired disease: age-related macular degeneration (AMD), occlusion of retinal vessels (ORV), etc.; birth defects: coloboma of choroid or optic nerve disc, etc.; hereditary diseases: retinitis pigmentosa, angioid streaks of the retina, etc.

4. A patient who has already undergone treatment (surgery, laser, etc.) for any disease of the retina: age-related macular degeneration (AMD), retinal vascular occlusion (ARV), etc. These patients should be excluded or allocated to a separate group.

200 participants were planed to include in study, 100 of them had diabetes mellitus and 100 participants were as a control group have risk factors for diabetes mellitus.

All participants were selected for the study based on the following inclusion and exclusion criteria