Detection of ataxia in low disability MS patients by hybrid convolutional neural networks based on images of plantar pressure distribution.

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.
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