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Evaluating subscapularis tendon tears on axillary lateral radiographs using deep learning.

Yusuhn Kang1, Dongjun Choi2, Kyong Joon Lee3

  • 1Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea. yskang0114@gmail.com.

European Radiology
|May 20, 2021
PubMed
Summary

A deep learning algorithm can now evaluate subscapularis tendon (SSC) tears using shoulder X-rays. This AI tool shows moderate accuracy in identifying tears, aiding in initial assessments and guiding further treatment decisions.

Keywords:
Deep learningRadiographyRotator cuff tearSubscapularis

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Orthopedics

Background:

  • Subscapularis tendon (SSC) tears are common shoulder injuries.
  • Accurate diagnosis is crucial for effective treatment planning.
  • Current diagnostic methods may have limitations.

Purpose of the Study:

  • To develop and validate a deep learning algorithm for assessing SSC tears.
  • To utilize axillary lateral shoulder radiography for tear evaluation.
  • To correlate imaging findings with clinical and surgical data.

Main Methods:

  • Trained a deep learning algorithm on 2,779 axillary lateral shoulder radiographs with arthroscopic-labeled data.
  • Input data included radiographs and patient clinical information.
  • Validated the algorithm on two independent test sets using arthroscopic and MRI findings.

Main Results:

  • Achieved areas under the curve (AUC) of 0.83 and 0.82 in the test sets.
  • Demonstrated high sensitivity (90.2-91.4%) and negative predictive value (89.5-90.4%).
  • Identified the subscapularis insertion site at the lesser tuberosity as a key indicator.

Conclusions:

  • The developed deep learning algorithm accurately assesses SSC tears from axillary lateral radiographs.
  • The algorithm provides an objective method for initial SSC integrity evaluation.
  • This tool can help identify patients who may benefit from further investigation or treatment.