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Updated: Jul 29, 2025

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography
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Deep learning-based screening tool for rotator cuff tears on shoulder radiography.

Ryosuke Iio1, Daiju Ueda2, Toshimasa Matsumoto3

  • 1Department of Orthopaedic Surgery, Graduate School of Medicine, Osaka City University, Osaka, Japan; Department of Orthopaedic Surgery, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan.

Journal of Orthopaedic Science : Official Journal of the Japanese Orthopaedic Association
|May 26, 2023
PubMed
Summary

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This study developed a deep learning AI tool to screen for rotator cuff tears using shoulder radiographs. The algorithm shows high accuracy for full-thickness tears, aiding early diagnosis.

Area of Science:

  • Orthopedic imaging
  • Artificial intelligence in medicine
  • Diagnostic radiology

Background:

  • Early diagnosis of rotator cuff tears is crucial for effective treatment.
  • Radiography is a common imaging technique but has limitations in accurately ruling out rotator cuff tears.
  • Deep learning (DL) AI is increasingly used in medical diagnostics.

Purpose of the Study:

  • To develop a DL algorithm for screening rotator cuff tears using shoulder radiography.
  • To evaluate the diagnostic performance of the DL algorithm.

Main Methods:

  • Utilized 2803 shoulder radiographs (true anteroposterior view).
  • Labeled radiographs as intact, low-grade partial-thickness, or high-grade/full-thickness rotator cuff tears based on arthroscopic findings.
Keywords:
Arthroscopic findingsArtificial intelligenceDeep learningRotator cuff tearsScreening toolShoulder radiography

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  • Assessed diagnostic performance using Area Under the Curve (AUC), sensitivity, Negative Predictive Value (NPV), and Negative Likelihood Ratio (LR-).

    • Main Results:

    • The DL algorithm achieved an AUC of 0.82, with high sensitivity (91.3%) and NPV (92.7%).
    • Demonstrated high diagnostic performance for full-thickness tears (sensitivity 94.5%, NPV 96.2%).
    • Showed lower performance for partial-thickness tears (sensitivity 78.9%, NPV 96.2%).

    Conclusions:

    • The developed DL algorithm demonstrates high diagnostic performance for screening full-thickness rotator cuff tears.
    • DL-based radiography can serve as an effective screening tool for rotator cuff tears when an appropriate cutoff value is established.