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Related Experiment Video

Updated: May 1, 2026

Metacarpal Small Incision for Carpal Tunnel Syndrome
04:08

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Machine learning-based prediction model for paresthesia improvement after open carpal tunnel release: a preliminary

Takahiro Yamazaki1, Yusuke Matsuura2, Takuto Takeda2

  • 1Department of Orthopaedic Surgery, Graduate school of Medicine, Chiba University, 1-8-1 Inohana Chuo-ku, Chiba, Chiba, 260-8670, Japan. taka-0407@hotmail.co.jp.

BMC Musculoskeletal Disorders
|April 30, 2026
PubMed
Summary

A new machine learning model accurately predicts paresthesia improvement after carpal tunnel surgery. Key factors include distal motor latency, age, and disease duration, aiding preoperative planning.

Keywords:
Carpal tunnel syndromeMachine learningParesthesiaPrediction modelSurgical outcomes

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Area of Science:

  • Orthopedics
  • Neurosurgery
  • Medical Informatics

Background:

  • Carpal tunnel syndrome (CTS) is a common hand neuropathy.
  • Surgery offers good outcomes, but persistent paresthesia occurs postoperatively.
  • Predicting postoperative paresthesia improvement is crucial for patient management.

Purpose of the Study:

  • Develop and evaluate a machine learning model to predict paresthesia improvement after carpal tunnel release surgery.
  • Identify key clinical factors influencing surgical outcomes.
  • Enhance preoperative counseling and treatment strategies.

Main Methods:

  • Retrospective analysis of 94 hands from 83 patients undergoing carpal tunnel release.
  • Paresthesia improvement assessed at 1-year follow-up on a 10-point scale (0-1 excellent).
  • Compared Support Vector Machine (SVM), Random Forest, and Logistic Regression models using AUC, accuracy, precision, and recall.

Main Results:

  • 56.4% of patients achieved excellent paresthesia improvement.
  • Optimized SVM model showed strong performance: AUC 0.852, accuracy 78.9%, precision 88.9%, recall 72.7%.
  • Top predictors: distal motor latency (DML), age, disease duration, thenar atrophy, thumb opposition deficit.

Conclusions:

  • A preliminary machine learning model accurately predicts excellent paresthesia improvement post-CTS surgery.
  • The model utilizes five easily accessible clinical parameters.
  • This pilot study supports developing advanced prediction tools for surgical planning.