ReviewMedical science monitor : international medical journal of experimental and clinical research2026
Carpal Tunnel Syndrome Diagnosis: A Narrative Review of Complementary Roles of Neuromuscular Ultrasound and Electrodiagnostic Studies.
Review in Medical science monitor : international medical journal of experimental and clinical research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Carpal tunnel syndrome (CTS), the most prevalent entrapment neuropathy of the upper extremities, presents diagnostic challenges due to nonspecific clinical symptoms and discrepancies between subjective experiences and objective findings. Traditional reliance on electrodiagnostic studies (EDX), including nerve conduction studies (NCS) and electromyography, as the traditional reference method for assessing median nerve function is limited by invasiveness, operator dependency, and reduced sensitivity in mild cases. Neuromuscular ultrasound (NMUS) has emerged as a non-invasive complementary imaging method, enabling visualization of morphological changes such as increased median nerve cross-sectional area (CSA) and restricted gliding. This narrative review synthesizes evidence on the diagnostic utility of NMUS and EDX. We found that while EDX remains the reference standard for functional grading (eg, distal motor latency), it is limited by its invasiveness. Conversely, NMUS excels in identifying anatomical pathologies (eg, CSA ≥10 mm²) but is operator dependent. Rather than using a single tool, we demonstrate that combining EDX's physiological precision with NMUS's anatomical visualization offers the highest diagnostic accuracy. Recent trends favor artificial intelligence (AI) integration and standardized multimodal protocols to bridge the current implementation gaps.
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