AI-assisted diagnosis of multiple sclerosis via optical coherence tomography

Abstract

Aims/Purpose: This study aims to evaluate the ability of inter-eye asymmetry and retinal structure thicknesses, obtained via the Posterior Pole protocol in Optical Coherence Tomography (OCT), to distinguish between healthy controls and patients with recently diagnosed multiple sclerosis (MS). Additionally, it aims to develop a convolutional neural network (CNN) for assisted diagnosis using these measurements.

Methods: Two cohorts, one with relapsing-remitting MS (RRMS) patients (n = 79) and another with healthy controls (n = 69), were recruited. Structural retinal measurements were obtained using the Spectralis OCT device. The analysis focused on thickness differences in nine retinal layers and their inter-eye asymmetry. Statistical methods included the Shapiro-Wilk test, Student's t-test, χ2 test, and area under the receiver operating characteristic (AUROC) curve. A CNN was trained to classify patients and controls based on the most discriminant retinal measurements.

Results: The study found significant thinning in the ganglion cell layer (GCL) and inner plexiform layer (IPL) of RRMS patients compared to controls, with AUROC values of 0.82 and 0.78, respectively. Inter-eye asymmetry was also notable in these layers, particularly the GCL (AUROC = 0.75) and IRL (AUROC = 0.74). The CNN, utilizing GCL thickness and IPL inter-eye difference as inputs, achieved an accuracy of 0.87, sensitivity of 0.82, and specificity of 0.92.

Conclusions: The study demonstrates that neuroretinal thinning and inter-eye differences in specific retinal layers, measurable by the Posterior Pole protocol in OCT, can effectively discriminate between MS patients and healthy controls. The CNN developed shows high accuracy in MS diagnosis, supporting the potential of OCT and AI integration in clinical settings. Further research is needed to validate these findings across larger and more diverse populations.