The macular retinal ganglion cell layer as a biomarker for diagnosis and prognosis in multiple sclerosis: A deep learning approach

• Published in: Acta ophthalmologica (Oxford, England) Vol. 102; no. 3; pp. e272 - e284
• Main Authors: Montolío, Alberto, Cegoñino, José, Garcia‐Martin, Elena, Pérez del Palomar, Amaya
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.05.2024
• Subjects: Biomarkers; Cross-Sectional Studies; Deep Learning; Diagnosis; Humans; Longitudinal Studies; Multiple sclerosis; Multiple Sclerosis - diagnosis; Neural networks; Neurodegeneration; optical coherence tomography; Prognosis; Retina; retinal ganglion cell layer; Retinal Ganglion Cells; Tomography, Optical Coherence - methods; deep learning; optical coherence tomography; multiple sclerosis; retinal ganglion cell layer
• ISSN: 1755-375X; 1755-3768; 1755-3768
• DOI: 10.1111/aos.15722

Purpose The macular ganglion cell layer (mGCL) is a strong potential biomarker of axonal degeneration in multiple sclerosis (MS). For this reason, this study aims to develop a computer‐aided method to facilitate diagnosis and prognosis in MS. Methods This paper combines a cross‐sectional study of 72 MS patients and 30 healthy control subjects for diagnosis and a 10‐year longitudinal study of the same MS patients for the prediction of disability progression, during which the mGCL was measured using optical coherence tomography (OCT). Deep neural networks were used as an automatic classifier. Results For MS diagnosis, greatest accuracy (90.3%) was achieved using 17 features as inputs. The neural network architecture comprised the input layer, two hidden layers and the output layer with softmax activation. For the prediction of disability progression 8 years later, accuracy of 81.9% was achieved with a neural network comprising two hidden layers and 400 epochs. Conclusion We present evidence that by applying deep learning techniques to clinical and mGCL thickness data it is possible to identify MS and predict the course of the disease. This approach potentially constitutes a non‐invasive, low‐cost, easy‐to‐implement and effective method.