Comparison of Machine Learning Methods Using Spectralis OCT for Diagnosis and Disability Progression Prognosis in Multiple Sclerosis

Montolío, A. (Universidad de Zaragoza) ; Cegoñino, J. (Universidad de Zaragoza) ; Garcia-Martin, E. ; Pérez del Palomar, A. (Universidad de Zaragoza)

Resumen: Machine learning approaches in diagnosis and prognosis of multiple sclerosis (MS) were analysed using retinal nerve fiber layer (RNFL) thickness, measured by optical coherence tomography (OCT). A cross-sectional study (72 MS patients and 30 healthy controls) was used for diagnosis. These 72 MS patients were involved in a 10-year longitudinal follow-up study for prognostic purposes. Structural measurements of RNFL thickness were performed using different Spectralis OCT protocols: fast macular thickness protocol to measure macular RNFL, and fast RNFL thickness protocol and fast RNFL-N thickness protocol to measure peripapillary RNFL. Binary classifiers such as multiple linear regression (MLR), support vector machines (SVM), decision tree (DT), k-nearest neighbours (k-NN), Naïve Bayes (NB), ensemble classifier (EC) and long short-term memory (LSTM) recurrent neural network were tested. For MS diagnosis, the best acquisition protocol was fast macular thickness protocol using k-NN (accuracy: 95.8%; sensitivity: 94.4%; specificity: 97.2%; precision: 97.1%; AUC: 0.958). For MS prognosis, our model with a 3-year follow up to predict disability progression 8 years later was the best predictive model. DT performed best for fast macular thickness protocol (accuracy: 91.3%; sensitivity: 90.0%; specificity: 92.5%; precision: 92.3%; AUC: 0.913) and SVM for fast RNFL-N thickness protocol (accuracy: 91.3%; sensitivity: 87.5%; specificity: 95.0%; precision: 94.6%; AUC: 0.913). This work concludes that measurements of RNFL thickness obtained with Spectralis OCT have a good ability to diagnose MS and to predict disability progression in MS patients. This machine learning approach would help clinicians to have valuable information. © 2022, The Author(s).

Idioma: Inglés

DOI: 10.1007/s10439-022-02930-3

Año: 2022

Publicado en: Annals of Biomedical Engineering 50, 5 (2022), 507-528
ISSN: 0090-6964

Factor impacto JCR: 3.8 (2022)

Categ. JCR: ENGINEERING, BIOMEDICAL rank: 47 / 96 = 0.49 (2022) - Q2 - T2

Factor impacto CITESCORE: 7.7 - Engineering (Q1)

Factor impacto SCIMAGO: 0.848 - Biomedical Engineering (Q2)

Financiación: info:eu-repo/grantAgreement/ES/ISCIII/PI17-01726
Financiación: info:eu-repo/grantAgreement/ES/MEC/BES-2017-080384
Financiación: info:eu-repo/grantAgreement/ES/MINECO/DPI2016-79302-R

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)