Automated detection and classification of the rotator cuff tear on plain shoulder radiograph using deep learning.

Journal: Journal of shoulder and elbow surgery
PMID: 38311106

Abstract

BACKGROUND: The diagnosis of rotator cuff tears (RCTs) using radiographs alone is clinically challenging; thus, the utility of deep learning algorithms based on convolutional neural networks has been remarkable in the field of medical imaging recognition. We aimed to evaluate the diagnostic performance of artificial intelligence (a deep learning algorithm; a convolutional neural network) to detect and classify RCTs using shoulder radiographs, and compare its diagnostic performance with that of orthopedic surgeons.

Authors

  • Eiko Hashimoto
    Department of Orthopedics Surgery, Chiba University, Chiba, Japan. Electronic address: eiko1014@hotmail.com.
  • Satoshi Maki
    Department of Orthopedic Surgery, Graduate School of Medicine, Chiba University, Japan; Center for Frontier Medical Engineering, Chiba University, Japan. Electronic address: satoshimaki@gmail.com.
  • Nobuyasu Ochiai
    Department of Orthopedics Surgery, Chiba University, Chiba, Japan.
  • Shohei Ise
    Department of Orthopedics Surgery, Chiba University, Chiba, Japan.
  • Kenta Inagaki
    Department of Orthopedics Surgery, Chiba University, Chiba, Japan.
  • Yu Hiraoka
    Department of Orthopedics Surgery, Chiba University, Chiba, Japan.
  • Fumiya Hattori
    Department of Orthopedics Surgery, Chiba University, Chiba, Japan.
  • Seiji Ohtori
    Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba, Japan.

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