ArticleBioengineering (Basel, Switzerland)2021
Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning.
Article in Bioengineering (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 15 citations in OpenAlex.
- Explainable Ensemble Learning for Robust Severity Stratification of Carpal Tunnel Syndrome from Clinical Data.Diagnostics (Basel, Switzerland) · 2026Article
- Intraoperative change in distal motor latency as a predictor for clinical outcome after mini-OCTR: a retrospective cohort study.Frontiers in neurology · 2025Article
- Carpal Tunnel Syndrome Automated Diagnosis: A Motor vs. Sensory Nerve Conduction-Based Approach.Bioengineering (Basel, Switzerland) · 2024Article
- Deep Learning-Based Approaches for Enhanced Diagnosis and Comprehensive Understanding of Carpal Tunnel Syndrome.Diagnostics (Basel, Switzerland) · 2023Article
- Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2023Article
- Machine learning and clinical neurophysiology.Journal of neurology · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent literature has revealed a long discussion about the importance and necessity of nerve conduction studies in carpal tunnel syndrome management. The purpose of this study was to investigate the possibility of automatic detection, based on electrodiagnostic features, for the median nerve mononeuropathy and decision making about carpal tunnel syndrome. The study included 38 volunteers, examined prospectively. The purpose was to investigate the possibility of automatically detecting the median nerve mononeuropathy based on common electrodiagnostic criteria, used in everyday clinical practice, as well as new features selected based on physiology and mathematics. Machine learning techniques were used to combine the examined characteristics for a stable and accurate diagnosis. Automatic electrodiagnosis reached an accuracy of 95% compared to the standard neurophysiological diagnosis of the physicians with nerve conduction studies and 89% compared to the clinical diagnosis. The results show that the automatic detection of carpal tunnel syndrome is possible and can be employed in decision making, excluding human error. It is also shown that the novel features investigated can be used for the detection of the syndrome, complementary to the commonly used ones, increasing the accuracy of the method.
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