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Related Experiment Video

Updated: Feb 26, 2026

Screening of Axonal Degeneration in Carpal Tunnel Syndrome Using Ultrasonography and Nerve Conduction Studies
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Noninvasive Assessment of the Ulnar Nerve in the Upper Extremity Using Machine Learning.

Akhil Dondapati1, Andrew Rodenhouse1, Thomas J Carroll1

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Summary

This study developed a machine learning algorithm for automated ultrasound analysis of the ulnar nerve in cubital tunnel syndrome diagnosis. The AI model accurately identifies and measures the nerve's cross-sectional area, improving diagnostic consistency.

Keywords:
Artificial intelligenceCubital tunnelMachine learningUlnar nerveUltrasound

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Neurology

Background:

  • Cubital tunnel syndrome diagnosis relies on ultrasound, but is limited by operator dependence and variability.
  • Automated analysis of ultrasound images can enhance diagnostic accuracy and consistency.

Purpose of the Study:

  • To develop a machine learning algorithm for automated identification, segmentation, and cross-sectional area (CSA) measurement of the ulnar nerve using ultrasound images.
  • To establish a foundational step towards operator-independent ultrasound assessment for cubital tunnel syndrome.

Main Methods:

  • A convolutional neural network (YOLOv8) was trained on ultrasound images of the cubital tunnel from 34 subjects (11 patients, 23 controls).
  • The model generated binary maps for automatic ulnar nerve detection and segmentation.
  • Accuracy was assessed using Dice scores and comparison of predicted vs. ground truth CSA measurements.

Main Results:

  • The YOLOv8 model achieved an average Dice score of 0.90, with 99% of images scoring above 0.75.
  • Mean absolute difference in CSA measurements between the model and ground truth was 2.08 mm² (16.05%).
  • The algorithm demonstrated high accuracy in identifying and measuring the ulnar nerve cross-sectional area.

Conclusions:

  • The developed machine learning algorithm represents a significant advancement in automating ultrasound-based diagnosis of cubital tunnel syndrome.
  • This approach minimizes operator dependence and technical variability, paving the way for more reliable and accessible diagnostic tools.
  • Further development can lead to fully automated, operator-independent ultrasound assessment of the ulnar nerve.