Resumen de Evaluation of machine learning algorithms and relevant biomarkers for the diagnosis of multiple sclerosis based on optical coherence tomography

Pablo García, Pilar Rojas, Nuria Díaz, Manuel Cadenas, Alberto Beltrán, J.J. Gómez Valverde

• Multiple sclerosis (MS) is a prevalent neurodegener- ative disease with significant visual pathway-related symptoms. Optical coherence tomography (OCT) has emerged as a valuable tool, and machine learning (ML) techniques hold promise for MS diagnosis. However, ex- isting studies often lack comprehensive feature exploita- tion and require interpretable model analysis to improve clinical insights and diagnostic criteria. This study evaluates machine learning models for classification of healthy controls and MS patients using a comprehensive set of macular and optic-disc parameters from OCT imaging. The study included a dataset of 77 MS eyes and 54 control eyes, obtained by ophthalmic examination and OCT measurements from Optic Disc and Macular Cube scan protocols of a Cirrus HD-OCT 5000 (Carl Zeiss, Meditec, Dublin, CA, USA). Our results identi- fied 19 features, validated by p-values (p < 0.001), as effective discriminators between MS patients and healthy controls. ...