Use of artificial intelligence to evaluate the detection of retinal alterations as a screening test in Mexican patients

In Mexico, chronic degenerative diseases are the leading cause of morbidity, which has frequent retina complications, being the leading cause of blindness in our population. Unfortunately, the detection of the pathology is usually late, resulting in more significant disability. To propose the detection of different pathologies with different artificial intelligence algorithms have been used for the images taken from the fundus of the eye. Objective. Evaluate different machine learning algorithms for the detection of retinal alterations in the Mexican population. Methodology. Evaluate two types of models to estimate artificial intelligence tools' screening capacity, one based on transfer learning and ensemble methods against one based only on convolutional networks. Results. We obtained good values to differentiate between healthy and sick but not to diagnose different pathologies. Conclusions. It is necessary to enlarge the imaging sample and to improve the screening models.