Ricerc@Sapienza

Background: Diabetes mellitus has a dramatically huge impact on healthcare systems and on patients¿ quality of life. Diabetic eye disease is the leading cause of vision loss in working-age adults and diabetic retinopathy (DR) represents the most common microvascular complication of the disease. Significant advancements have been made in the last decades regarding prevention and early diagnosis. However, about one third of diabetic patients are affected by ocular complications, one third of which are vision-threatening. The constantly increasing prevalence of diabetes and its growing burden on public health demand the creation of a mass screening programme, for the ophthalmologist to be able to fully invest resources and care on patients with DR.
Purpose: the present study aims to build a powerful and effective screening tool for DR, through the development of a portable, smart, non-mydriatic fundus camera with an in-built deep learning detection system. The main task of such a device would be to identify patients with signs of ocular complications and to refer them to an ophthalmology department. The ideal use case scenarios are represented by pharmacies and general practitioners.
Design: observational. Duration 12 months.
Methods: Open access retinal image databases will be used for the training of a deep-learning algorithm aimed at differentiating between healthy and affected retinas. 250 patients with type I and II diabetes examined in the Ophthalmology Clinic of Policlinico Umberto I will undergo a fundus photo through a non-mydriatic fundus camera. The obtained images will be validated by an independent ophthalmologist in order to assess the accuracy of the algorithm.
Results: the initial evaluation by expert ophthalmologists will allow to validate the accuracy of the device and its further use as a mass screening tool for diabetic retinopathy.