Citation: López-Dorado, A.; Ortiz,
M.; Satue, M.; Rodrigo, M.J.; Barea,
R.; Sánchez-Morla, E.M.; Cavaliere,
C.; Rodríguez-Ascariz, J.M.;
Orduna-Hospital, E.; Boquete, L.;
et al. Early Diagnosis of Multiple
Sclerosis Using Swept-Source Optical
Coherence Tomography and
Convolutional Neural Networks
Trained with Data Augmentation.
Sensors 2022, 22, 167. https://
doi.org/10.3390/s22010167
Academic Editors: Juan
Pablo Martínez and
Andrea Facchinetti
Received: 3 November 2021
Accepted: 22 December 2021
Published: 27 December 2021
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Article
Early Diagnosis of Multiple Sclerosis Using Swept-Source
Optical Coherence Tomography and Convolutional Neural
Networks Trained with Data Augmentation
Almudena López-Dorado
1
, Miguel Ortiz
2
, María Satue
3
, María J. Rodrigo
3
, Rafael Barea
1
,
Eva M. Sánchez-Morla
4,5,6
, Carlo Cavaliere
1
, José M. Rodríguez-Ascariz
1
, Elvira Orduna-Hospital
3
,
Luciano Boquete
1,
* and Elena Garcia-Martin
3,
*
1
Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28801 Alcalá de Henares,
Spain; almudena.lopez@uah.es (A.L.-D.); rafael.barea@uah.es (R.B.); carlo.cavaliere@uah.es (C.C.);
jmr.ascariz@uah.es (J.M.R.-A.)
2
Computer Vision, Imaging and Machine Intelligence Research Group, Interdisciplinary Center for Security,
Reliability and Trust (SnT), University of Luxembourg, 4365 Luxembourg, Luxembourg;
miguel.ortizdelcastillo@uni.lu
3
Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Department of Ophthalmology,
Aragon Institute for Health Research (IIS Aragon), Miguel Servet University Hospital, University of Zaragoza,
50018 Zaragoza, Spain; mariasatue@gmail.com (M.S.); mariajesusrodrigo@hotmail.es (M.J.R.);
elvira.orduna@gmail.com (E.O.-H.)
4
Department of Psychiatry, Hospital 12 de Octubre Research Institute (i+12), 28041 Madrid, Spain;
evamas01@ucm.es
5
Faculty of Medicine, Complutense University of Madrid, 28040 Madrid, Spain
6
Biomedical Research Networking Centre in Mental Health (CIBERSAM), 28029 Madrid, Spain
* Correspondence: luciano.boquete@uah.es (L.B.); egmvivax@yahoo.com (E.G.-M.)
Abstract:
Background: The aim of this paper is to implement a system to facilitate the diagnosis of
multiple sclerosis (MS) in its initial stages. It does so using a convolutional neural network (CNN)
to classify images captured with swept-source optical coherence tomography (SS-OCT). Methods:
SS-OCT images from 48 control subjects and 48 recently diagnosed MS patients have been used. These
images show the thicknesses (45
×
60 points) of the following structures: complete retina, retinal
nerve fiber layer, two ganglion cell layers (GCL+, GCL++) and choroid. The Cohen distance is used to
identify the structures and the regions within them with greatest discriminant capacity. The original
database of OCT images is augmented by a deep convolutional generative adversarial network to
expand the CNN’s training set. Results: The retinal structures with greatest discriminant capacity are
the GCL++ (44.99% of image points), complete retina (26.71%) and GCL+ (22.93%). Thresholding
these images and using them as inputs to a CNN comprising two convolution modules and one
classification module obtains sensitivity = specificity = 1.0. Conclusions: Feature pre-selection and the
use of a convolutional neural network may be a promising, nonharmful, low-cost, easy-to-perform
and effective means of assisting the early diagnosis of MS based on SS-OCT thickness data.
Keywords:
multiple sclerosis; optical coherence tomography; convolutional neural network; generative
adversarial network
1. Introduction
In recent years, the usefulness of deep learning (DL) techniques has been demon-
strated in many applications, including in medicine [
1
]. Most medical applications have
been in specialties related to diagnostic imaging, such as radiology or dermatology [
1
–
6
],
in genomics [
7
,
8
] and, in lower numbers, in one-dimensional signal analysis, such as elec-
troencephalograms (EEG) [
9
–
13
], electrodermal activity [
14
] and electrocardiograms (ECG)
used in arrhythmia classification [15,16].
Sensors 2022, 22, 167. https://doi.org/10.3390/s22010167 https://www.mdpi.com/journal/sensors
