Retinal age as a predictive biomarker of the diabetic retinopathy grade

To apply artificial intelligence (AI) techniques, through deep learning algorithms, for the development and optimization of a system for predicting the age of a person based on a color retinography and to study a possible relationship between the evolution of retinopathy diabetes and premature ageing of the retina. A convolutional network was trained to calculate the age of a person based on a retinography. Said training was carried out on a set of retinographies of patients with diabetes previously divided into three subsets (training, validation and test). The difference between the chronological age of the patient and the biological age of the retina was defined as the retinal age gap. A set of 98,400 images was used for the training phase, 1000 images for the validation phase and 13,544 for the test phase. The retinal gap of the patients without DR was 0.609 years and that of the patients with DR was 1905 years (p