Evaluation of machine learning algorithms and relevant biomarkers for the diagnosis of multiple sclerosis based on optical coherence tomography
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García Mesa, Pablo; Rojas Lozano, Pilar; Díaz Gutiérrez, Nuria; Cadena Santoyo, Manuel; Beltrán Carrero, Alberto J.; [et al.]Área de conocimiento
Tecnología ElectrónicaPatrocinadores
This research is part of the grant TED2021-131951B-I00 funded by MCIN/AEI/10.13039/501100011033 and by the the “European Union NextGenerationEU/PRTR” and the grant ”Primeros Proyetos” funded by the ETSI Telecomunicaci´on (Universidad Polit´ecnica de Madrid).Fecha de publicación
2023-11Editorial
Universidad Politécnica de CartagenaCita bibliográfica
GARCÍA MESA, Pablo, et. al. Evaluation of machine learning algorithms and relevant biomarkers for the diagnosis of multiple sclerosis based on optical coherence tomography. En: XLI Congreso Anual de la Sociedad Española de Ingeniería Biomédica. Cartagena: Universidad Politécnica de Cartagena, 2023. Pp. 356-359. ISBN: 978-84-17853-76-1Palabras clave
Machine learningMultiple sclerosis (MS)
Optical coherence tomography (OCT)
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. ...
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