ArticleScientific reports2021
Machine learning-based approach for disease severity classification of carpal tunnel syndrome.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 45 citations in OpenAlex.
- Artificial intelligence in ultrasound imaging for carpal tunnel syndrome: what clinicians need to know.Journal of medical ultrasonics (2001) · 2026Review
- Explainable Ensemble Learning for Robust Severity Stratification of Carpal Tunnel Syndrome from Clinical Data.Diagnostics (Basel, Switzerland) · 2026Article
- Development of a machine learning model using structured and unstructured features for predicting surgery among patients with carpal tunnel syndrome: development and validation.Scientific reports · 2026Article
- A neuromuscular clinician's primer on machine learning.Journal of neuromuscular diseases · 2026Review
- Long-term patient-centered outcomes following carpal tunnel release surgery: a 10-year follow-up.Langenbeck's archives of surgery · 2025Article
- Intraoperative change in distal motor latency as a predictor for clinical outcome after mini-OCTR: a retrospective cohort study.Frontiers in neurology · 2025Article
- Article
- Application of radiomics model based on ultrasound image features in the prediction of carpal tunnel syndrome severity.Skeletal radiology · 2024Article
- Carpal Tunnel Syndrome Automated Diagnosis: A Motor vs. Sensory Nerve Conduction-Based Approach.Bioengineering (Basel, Switzerland) · 2024Article
- Diagnostic Accuracy of Screening Tests for Diabetic Peripheral Neuropathy: An Umbrella Review.Journal of diabetes research · 2024Review
- Transforming medicine: artificial intelligence integration in the peripheral nervous system.Frontiers in neurology · 2024Review
- Carpal tunnel syndrome prediction with machine learning algorithms using anthropometric and strength-based measurement.PloS one · 2024Article
- In-hospital fall prediction using machine learning algorithms and the Morse fall scale in patients with acute stroke: a nested case-control study.BMC medical informatics and decision making · 2023Article
- Deep Learning-Based Approaches for Enhanced Diagnosis and Comprehensive Understanding of Carpal Tunnel Syndrome.Diagnostics (Basel, Switzerland) · 2023Article
- Characteristics of diabetic and non-diabetic carpal tunnel syndrome in terms of clinical, electrophysiological, and Sonographic features: a cross-sectional study.BMC musculoskeletal disorders · 2023Article
- Feasibility of artificial intelligence assisted quantitative muscle ultrasound in carpal tunnel syndrome.BMC musculoskeletal disorders · 2023Article
- Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2023Article
- Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts.Lancet (London, England) · 2023Article
- Classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms.Frontiers in surgery · 2022Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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