Using an eye tracker with the patient, we could know which point of the screen that the patient is looking at, but analyzing gaze data of the patient is so complicated, that is why we choose machine learning to help ophthalmologists to diagnose visual pathologies. We could collect data by performing various tests on the patients, and the ophthalmologist supervises this operation.
MakWe used that gaze data to feed into the ML model, and some tests also made use of the ASA (Accelerated Stochastic Approximation) algorithm to provide meaningful constants before using in the ML model. Outputs of the model are the probability of abnormalities for each eye and each pathology.e the live performance even more engaging with FanReact.