Towards Diagnosis of Carpal Tunnel Syndrome Using Machine Learning

Wei, Yuan; Zhang, Wei; Gu, Feng

doi:10.1145/3442536.3442549

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…Therefore, confirmation of severity grade is essential to the determination of treatment options. Meanwhile, Wei et al 24 also reported the ML-base CTS assessment. They identified that hand kinematic features were important for CTS diagnosis and severity grading using RF in controls, mild-, and moderate-CTS hands.…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based approach for disease severity classification of carpal tunnel syndrome

Park

Kim

Lee

et al. 2021

Sci Rep

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Identifying the severity of carpal tunnel syndrome (CTS) is essential to providing appropriate therapeutic interventions. We developed and validated machine-learning (ML) models for classifying CTS severity. Here, 1037 CTS hands with 11 variables each were retrospectively analyzed. CTS was confirmed using electrodiagnosis, and its severity was classified into three grades: mild, moderate, and severe. The dataset was randomly split into a training (70%) and test (30%) set. A total of 507 mild, 276 moderate, and 254 severe CTS hands were included. Extreme gradient boosting (XGB) showed the highest external validation accuracy in the multi-class classification at 76.6% (95% confidence interval [CI] 71.2–81.5). XGB also had an optimal model training accuracy of 76.1%. Random forest (RF) and k-nearest neighbors had the second-highest external validation accuracy of 75.6% (95% CI 70.0–80.5). For the RF and XGB models, the numeric rating scale of pain was the most important variable, and body mass index was the second most important. The one-versus-rest classification yielded improved external validation accuracies for each severity grade compared with the multi-class classification (mild, 83.6%; moderate, 78.8%; severe, 90.9%). The CTS severity classification based on the ML model was validated and is readily applicable to aiding clinical evaluations.

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Section: Discussionmentioning

confidence: 99%

Machine learning-based approach for disease severity classification of carpal tunnel syndrome

Park

Kim

Lee

et al. 2021

Sci Rep

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Leveraging wearable sensors and machine learning for posture-based detection of carpal tunnel syndrome

Gautam,

Gupta,

Shrivastava

2024

Eng. Res. Express

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Carpal tunnel is associated with long-term use of the wrist and hand for various activities such as typing, welding, or poor working postures. Carpal Tunnel Syndrome (CTS) may cause severe pain and discomfort in the hand and wrist, and in some circumstances, surgery becomes inevitable. The objective of this study is to prevent typing postures, which can be ascertained as predisposing subjects to CTS development. The data used in this study is an array of wrist wearable sensors to capture flexion, extension, and bending of fingers while using a keyboard or mouse.In this research, conducted an exploratory data analysis (EDA) to gain an initial understanding of the dataset. Following the exploratory phase and applied feature extraction techniques, specifically Principal Component Analysis (PCA). As put forward for the proposed research, the strategies to prevent risky occupations have broad potential at the present time, especially in the case of CTS when preventing repetitive wrist movements. Machine learning is employed on the data in order to identify risk factors indicative of a high probability of CTS. The analyzed models are linear regression, Support Vector Machine, Random Forest, Multilayer Perceptron, Convolution Neural Network, and Long Short Term Memory. The conditions for assessing the performance of the data models include RMS error, coefficients of determination, and mean absolute percentage error.

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Video Analysis of Hand Gestures for Distinguishing Patients with Carpal Tunnel Syndrome

Matsui

Ibara

Tsukamoto

et al. 2022

Companion Proceedings of the 2022 Conference on Interactive Surfaces and Spaces

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Towards Diagnosis of Carpal Tunnel Syndrome Using Machine Learning

Cited by 4 publications

References 18 publications

Machine learning-based approach for disease severity classification of carpal tunnel syndrome

Machine learning-based approach for disease severity classification of carpal tunnel syndrome

Leveraging wearable sensors and machine learning for posture-based detection of carpal tunnel syndrome

Video Analysis of Hand Gestures for Distinguishing Patients with Carpal Tunnel Syndrome

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