Analysis of static plantar pressure data with capsule networks: Diagnosing ataxia in MS patients with a deep learning-based approach

Abstract

In this study, it was aimed to detect ataxia in patients with Multiple Sclerosis (MS) by utilizing static plantar pressure data and capsule networks (CapsNet), one of the deep learning (DL) architectures. CapsNet is also equipped with a robust dynamic routing mechanism that determines the output of the next capsule. MS is a chronic nervous system disease that shows its effect in the central nervous system and manifests itself with attacks. One of the most common and challenging symptoms of MS is known as ataxia. Ataxia causes loss of control of limb muscle tone or gait disorders, leading to loss of balance and coordination. The diagnosis of ataxia in MS is applied employing the standard Expanded Disability Status Scale (EDSS) score. However, due to reasons such as physician misconception, diagnosis differences among physicians, and incorrect patient information, more unbiased solutions are required for the diagnosis. The results included Sensitivity at 96.34 % ± 1.71, Specificity at 98.11 % ± 2.04, Precision at 98.08 % ± 2.16, and Accuracy at 97.13 % ± 0.33. The main motivation of the study is to show that these deep learning methods can successfully detect ataxia in MS patients using static plantar pressure data. The high-performance measurements of sensitivity, specificity, precision and accuracy emphasize that the proposed system can be an effective tool in clinical practice. In addition, it was concluded that the proposed autonomous system would be a support mechanism to assist the physician in the detection of ataxia in patients with MS.