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•Diagnosis of active MS lesions using gadolinium-based MRI has complications•By using deep learning, active lesions can be distinguished from inactive ones•A convolutional neural network predicts active lesions•The use of pre-trained networks can detect active lesions

Optical coherence tomography (OCT) investigations have revealed that the thickness of inner retinal layers becomes decreased in multiple sclerosis (MS) patients, compared to healthy control (HC) individuals. To date, a number of studies have applied machine learning to OCT thickness measurements, aiming to enable accurate and automated diagnosis of the disease. However, there have much less emphasis on other less common retinal imaging modalities, like infrared scanning laser ophthalmoscopy (IR-SLO), for classifying MS. IR-SLO uses laser light to capture high-resolution fundus images, often performed in conjunction with OCT to lock B-scans at a fixed position.

•A cross-sectional population-based study in Isfahan, Iran•OCT in HCs, MS with and without ON, and NMOSD with and without ON•No significant difference to distinguish NMOSD and RRMS patients•All quadrants of pRNFL in the RRMS and NMOSD groups were thinner than HCs•GCL and total macula were significantly less than HC in all NMOSD and RRMS eyes