Highlights
- •Using an image dataset containing dynamic plantar pressure distribution, ataxia can be detected in MS patients with low disability using the convolutional neural network (CNN) approach.
- •% 89.23 accuracy was achieved with the VGG19-SVM hybrid model.
- •By detecting ataxia in low disability MS patients, it is an alternative and objective method that supports physicians to make an early decision about the disease.
Abstract
Background
This study aimed to detect ataxia in patients with multiple sclerosis (PwMS) with
a deep learning-based approach based on images showing plantar pressure distribution
of the patients. The secondary aim of the study was to investigate an alternative
and objective method in the early diagnosis of ataxia in these patients.
Methods
A total of 105 images showing plantar pressure distribution of 43 ataxic PwMS and
62 healthy individuals were analyzed. The images were resized for the models including
VGG16, VGG19, ResNet, DenseNet, MobileNet, NasNetMobile, and NasNetLarge. Feature
vectors were extracted from the resized images and then classified using Support Vector
Machines (SVM), K-Nearest Neighbors (K-NN), and Artificial Neural Network (ANN). A
10-fold cross-validation was applied to increase the validity of the classifiers.
Results
The VGG19-SVM hybrid model showed the highest accuracy, sensitivity, and specificity
values (89.23%, 89.65%, and 88.88%, respectively).
Conclusion
The proposed method provided an automatic decision support system for detecting ataxia
based on images showing plantar pressure distribution in patients with PwMS. The performance
of the proposed method indicated that this method can be applied in clinical practice
to establish a rapid diagnosis of ataxia that is asymptomatic or difficult to detect
clinically and that it can be recommended as a useful aid for the physician in clinical
practice.
Keywords
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Article info
Publication history
Published online: September 14, 2021
Accepted:
September 9,
2021
Received in revised form:
August 25,
2021
Received:
May 17,
2021
Identification
Copyright
© 2021 Elsevier B.V. All rights reserved.
