Artificial Intelligence for the Detection of Central Retinal Disease and Non-mydriatic Glaucoma in the Context of Patients With Diabetes Mellitus in Primary Care: A Prospective Study Comparing the Diagnostic Capacity of an AI Algorithm

Detailed Summary

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 RD 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 will consist of carrying out two studies simultaneously: 1. Development of an algorithm with artificial intelligence to detect signs of DR, other pathologies of the central retina and glaucoma in patients with diabetes. 2. Carrying out a prospective study that will make it possible to compare the diagnostic capacity of the algorithms with that of the family medicine specialists who read the background images. The reference will be double-blind reading by ophthalmologists who specialize in retina. Cession of the images began at the end of 2018. The development of the AI algorithm is calculated to last about 3 to 4 months. Inclusion of patients in the cohort will start in early 2019 and is expected to last 3 to 4 months. Preliminary results are expected to be published by the end of 2019. The study will allow the development of an algorithm based on AI that can demonstrate an equal or superior performance, and that constitutes a complement or an alternative, to the current screening of DR in diabetic patients

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