Can deep learning revolutionize clinical understanding and diagnosis of optic neuropathy?

Abstract Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. Deep Learning has been widely adopted in speech and image recognition, natural language processing which has an impact on healthcare. In the recent decade, the application of DL has exponentially grown in the field of Ophthalmology. The fundoscopy, slit lamp photography, optical coherence tomography (OCT), and magnetic resonance imaging (MRI) were employed for clinical examination of various ocular conditions. These data served as a perfect platform for the development of DL models in Ophthalmology. Currently, the application of DL in ocular disorders is majorly studied in Diabetic retinopathy (DR), age-related macular degeneration (AMD), macular edema, retinopathy of prematurity (ROP), glaucoma, and cataract. In Ophthalmology, DL models are gradually expanding their scope in optic neuropathies. Glaucoma and optic neuritis are optic nerve disorders, where DL models are currently studied for clinical applications. For further expansion of DL application in inherited optic neuropathies, we discussed the recent observational studies revealing the pathophysiological changes at the optic nerve in Leber's hereditary optic neuropathy (LHON). LHON is an inherited optic neuropathy leading to bilateral loss of vision in early age groups. Hence for early management, further footsteps in the application of DL in LHON will benefit both ophthalmologists and patients. In this review, we discuss the recent advancements of AI in the Ophthalmology and prospective of applying DL models in LHON for clinical precision and timely diagnosis.