1. Neural networks to identify multiple sclerosis with optical coherence tomography

    Neural networks to identify multiple sclerosis with optical coherence tomography

    Purpose: To compare axonal loss in ganglion cells detected with spectral-domain optical coherence tomography (OCT) in eyes of patients with multiple sclerosis (MS) versus healthy control subjects using an artificial neural network (ANN). To analyse the capability of the ANN technique to improve the detection of retinal nerve fibre layer (RNFL) damage in patients with multiple sclerosis. Methods: Patients with multiple sclerosis (n = 106) and age-matched healthy subjects (n = 115) were enrolled. The Spectralis OCT system was used to obtain the circumpapillary RNFL thickness in both eyes. The 768 RNFL thickness measurements provided by the Spectralis OCT were performed to ...

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