Clinical Research Directory

AI for the Detection of Retinal Disease and Glaucoma in Patients With Diabetes Mellitus in Primary Care

Background: Diabetic retinopathy (DR) is one of the most important causes of blindness worldwide, especially in developed countries. In diabetic patients, periodic examination of the back of the eye using a nonmydriatic camera has been widely demonstrated to be an effective system to control and prevent the onset of DR. Convolutional neural networks have been used to detect DR, achieving very high sensitivities and specificities. Hypothesis: It is possible to develop algorithms based on artificial intelligence that can demonstrate equal or superior performance and that constitute an alternative to the current screening of DR and other ophthalmic pathologies in diabetic patients. Objectives: * Development of an artificial intelligence system for the detection of signs of retinal pathology and other ophthalmic pathologies in diabetic patients. * Scientific validation of the system to be used as a screening system in primary care. Methods: This project consisted of carrying out two studies simultaneously: 1. Development of an algorithm with artificial intelligence to detect signs of DR and other pathologies of the central retina in patients with diabetes. 2. An observational, cross-sectional study comparing the diagnostic capacity of the algorithms with that of the family medicine specialists who read the fundus images. The reference was double-blind reading by ophthalmologists who specialize in retina. The cession of the images began at the end of 2018. The images used for the validation were obtained during routine diabetic retinopathy screening between May and August 2021. The results have since been published. The study allowed the development of an algorithm based on AI able to demonstrate an equal or superior performance, and to constitute a complement or an alternative to the current screening of DR in diabetic patients.