Development and clinical validation of deep learning for auto-diagnosis of supraspinatus tears.

Journal: Journal of orthopaedic surgery and research
PMID: 37308995

Abstract

BACKGROUND: Accurately diagnosing supraspinatus tears based on magnetic resonance imaging (MRI) is challenging and time-combusting due to the experience level variability of the musculoskeletal radiologists and orthopedic surgeons. We developed a deep learning-based model for automatically diagnosing supraspinatus tears (STs) using shoulder MRI and validated its feasibility in clinical practice.

Authors

  • Deming Guo
    Orthpoeadic Medical Center, Jilin University Second Hospital, Changchun, Jilin, China (mainland).
  • Xiaoning Liu
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Middletown, NJ, USA.
  • Dawei Wang
    Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China.
  • Xiongfeng Tang
    Orthpoeadic Medical Center, Jilin University Second Hospital, Changchun, Jilin, China (mainland).
  • Yanguo Qin
    Orthopedic Medical Center, Jilin University Second Hospital, Changchun, Jilin, China (mainland).

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