Retinal age as a predictive biomarker of the diabetic retinopathy grade.

ABSTRACT

OBJECTIVE: 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. METHODS: 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. RESULTS: 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 < 0.001), with the distribution by degree of DR being mild DR 1541 years, moderate DR 3017 years, DR severe 3117 years and proliferative DR 8583 years. CONCLUSIONS: The retinal age gap shows a positive mean difference between diabetics with DR versus those without DR, and it increases progressively, according to the degree of DR. These results could indicate the existence of a relationship between the evolution of the disease and premature ageing of the retina.