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

Clinical applications of deep learning in neuroinflammatory diseases: A scoping review

BACKGROUND

Deep learning (DL) is an artificial intelligence technology that has aroused much excitement for predictive medicine due to its ability to process raw data modalities such as images, text, and time series of signals.

OBJECTIVES

Here, we intend to give the clinical reader elements to understand this technology, taking neuroinflammatory diseases as an illustrative use case of clinical translation efforts. We reviewed the scope of this rapidly evolving field to get quantitative insights about which clinical applications concentrate the efforts and which data modalities are most commonly used.

METHODS

We queried the PubMed database for articles reporting DL algorithms for clinical applications in neuroinflammatory diseases and the radiology.healthairegister.com website for commercial algorithms.

RESULTS

The review included 148 articles published between 2018 and 2024 and five commercial algorithms. The clinical applications could be grouped as computer-aided diagnosis, individual prognosis, functional assessment, the segmentation of radiological structures, and the optimization of data acquisition. Our review highlighted important discrepancies in efforts. The segmentation of radiological structures and computer-aided diagnosis currently concentrate most efforts with an overrepresentation of imaging. Various model architectures have addressed different applications, relatively low volume of data, and diverse data modalities. We report the high-level technical characteristics of the algorithms and synthesize narratively the clinical applications. Predictive performances and some common a priori on this topic are finally discussed.

CONCLUSION

The currently reported efforts position DL as an information processing technology, enhancing existing modalities of paraclinical investigations and bringing perspectives to make innovative ones actionable for healthcare.

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

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.

Detection of ataxia with hybrid convolutional neural network using static plantar pressure distribution model in patients with multiple sclerosis

OBJECTIVE

In this study, it is aimed to detect ataxia for Persons with Multiple Sclerosis (PwMS) through a deep learning-based approach using an image dataset containing static plantar pressure distribution. Here, an alternative and objective method will be proposed to assist physicians who diagnose PwMS in the early stages.

METHODS

A total of 406 static bipedal pressure distribution image data for 43 ataxic PwMS and 62 healthy individuals were used in the study. After preprocessing, these images were given as input to pre-trained deep learning models such as VGG16, VGG19, ResNet, DenseNet, MobileNet, and NasNetMobile. The data of each model is utilized to generate its feature vectors. Finally, feature vectors obtained from static pressure distribution images were classified by SVM (Support Vector Machine), K-NN (K-Nearest Neighbors), and ANN (Artificial Neural Network). In addition, a cross-validation method was used to examine the validity of the classifier.

RESULTS

The performance of the proposed models was evaluated with accuracy, sensitivity, specificity, and F1-measure criteria. The VGG19-SVM hybrid model showed the best performance with 95.12% acc, 94.91% sen, 95.31% spe, and 94.44% F1.

CONCLUSIONS

In this study, a specific and sensitive automatic test evaluation system was proposed for Ataxic syndromes using digital images to observe the motor skills of the subjects. Comparative results show that the proposed method can be applied in practice for ataxia that is clinically difficult to detect or not yet symptomatic. It can be defined using only static plantar pressure distribution in the early stage and it can be recommended as an assistant system to physicians in clinical practice.