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

• García Mesa, Pablo ^[1] ; Rojas Lozano, Pilar ; Díaz Gutiérrez, Nuria ; Cadena Santoyo, Manuel ; Beltrán Carrero, Alberto J. ^[1] ; Juan J. Gómez-Valverde ^[1]
  1. [1] Universidad Politécnica de Madrid

     Universidad Politécnica de Madrid

     Madrid, España

     
• Localización: CASEIB 2023. Libro de Actas del XLI Congreso Anual de la Sociedad Española de Ingeniería Biomédica: Contribuyendo a la salud basada en valor / coord. por Joaquín Roca González, Dolores Ojados González, Juan Suardíaz Muro, 2023, ISBN 978-84-17853-76-1, págs. 356-359
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen

  • 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. ...