Deep Learning Estimation of Median Nerve Volume Using Ultrasound Imaging in a Human Cadaver Model

Tomoyuki Kuroiwa, Jaidip Jagtap, Julia Starlinger, Hayman Lui, Zeynettin Akkus, Bradley Erickson, Peter Amadio

Research output: Contribution to journalArticlepeer-review


Median nerve swelling is one of the features of carpal tunnel syndrome (CTS), and ultrasound measurement of maximum median nerve cross-sectional area is commonly used to diagnose CTS. We hypothesized that volume might be a more sensitive measure than cross-sectional area for CTS diagnosis. We therefore assessed the accuracy and reliability of 3-D volume measurements of the median nerve in human cadavers, comparing direct measurements with ultrasound images interpreted using deep learning algorithms. Ultrasound images of a 10-cm segment of the median nerve were used to train the U-Net model, which achieved an average volume similarity of 0.89 and area under the curve of 0.90 from the threefold cross-validation. Correlation coefficients were calculated using the areas measured by each method. The intraclass correlation coefficient was 0.86. Pearson's correlation coefficient R between the estimated volume from the manually measured cross-sectional area and the estimated volume of deep learning was 0.85. In this study using deep learning to segment the median nerve longitudinally, estimated volume had high reliability. We plan to assess its clinical usefulness in future clinical studies. The volume of the median nerve may provide useful additional information on disease severity, beyond maximum cross-sectional area.

Original languageEnglish (US)
Pages (from-to)2237-2248
Number of pages12
JournalUltrasound in Medicine and Biology
Issue number11
StatePublished - Nov 2022


  • 3-D imaging
  • Area measurement
  • Artificial intelligence
  • Cadaver
  • Carpal tunnel syndrome
  • Deep learning
  • Median nerve
  • Ultrasonographic imaging
  • Ultrasound
  • Volume measurement

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Biophysics
  • Acoustics and Ultrasonics


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