OCT (optical coherence tomography) has significantly advanced the clinical practice of neuro-ophthalmology, including the care of patients with demyelinating disease. OCT is a non-invasive, reliable, financially accessible technique for visualizing structures in the eye including the retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (GCIPL) of the retina. Because the eye is an anterior extension of the brain, visualizing the eye with OCT allows clinicians to gain insight into disease burden and progression in patients with demyelinating disease. Importantly, the updated 2024 McDonald Criteria include the optic nerve as a lesion site for diagnosis of MS. Paraclinical tests like OCT and VEP (visual evoked potential) can be employed in this setting. OCT is currently being studied in the diagnosis and care of patients with MOGAD and NMOSD as well. For example, recent studies have employed OCT to differentiate MOGAD from other conditions like NAION (non-arteritic ischemic optic neuropathy) in the correct clinical context. Machine learning approaches focused on OCT are poised to advance our understanding of the role that such testing can play in the diagnosis and management of these conditions. Level of Information: Intermediate, AdvancedOCT (optical coherence tomography) has significantly advanced the clinical practice of neuro-ophthalmology, including the care of patients with demyelinating disease. OCT is a non-invasive, reliable, financially accessible technique for visualizing structures in the eye including the retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (GCIPL) of the retina. Because the eye is an anterior extension of the brain, visualizing the eye with OCT allows clinicians to gain insight into disease burden and progression in patients with demyelinating disease. Importantly, the updated 2024 McDonald Criteria include the optic nerve as a lesion site for diagnosis of MS. Paraclinical tests like OCT and VEP (visual evoked potential) can be employed in this setting. OCT is currently being studied in the diagnosis and care of patients with MOGAD and NMOSD as well. For example, recent studies have employed OCT to differentiate MOGAD from other conditions like NAION (non-arteritic ischemic optic neuropathy) in the correct clinical context. Machine learning approaches focused on OCT are poised to advance our understanding of the role that such testing can play in the diagnosis and management of these conditions. Level of Information: Intermediate, Advanced
