Deep Learning-Based OCT for Epilepsy: A Review
Author(s): Rukayya Muhammad, Moussa Mahamat Boukar, Steve Adeshina and Senol Dane*
Abstract
Automating screening and diagnosis in the medical field saves time and decreases the likelihood of misdiagnosis whilst saving physicians cost and labor time. Deep learning methods development and their feasibility have enabled machines to interpret complex features in medical data, leading to rapid advances in automation. This research discusses how deep learning has been applied to Optical Coherence Tomography in epileptic patients to improve clinical diagnostic categorization, treatment outcome prediction, and comprehension of cognitive comorbidities. It covers the present deep learning research's strengths and weaknesses, as well as the necessity for further studies employing constrained datasets to evaluate the repeatability and generalizability of existing findings, as well as studies to test the clinical utility of deep learning methodologies.