Improving Deep Learning-Based Grading of Partial-thickness Supraspinatus Tendon Tears with Guided Diffusion Augmentation.

Journal: Academic radiology
Published Date: May 19, 2025

Abstract

RATIONALE AND OBJECTIVES: To develop and validate a deep learning system with guided diffusion-based data augmentation for grading partial-thickness supraspinatus tendon (SST) tears and to compare its performance with experienced radiologists, including external validation.

Authors

  • Ming Ni
    Department of Orthopaedics, Chinese People's Liberation Army General Hospital (301 Hospital), 28 Fuxing Rd, 100853, Beijing, China.
  • Dina Jiesisibieke
    Department of Radiology, Peking University Third Hospital, No. 49 Huayuan North Road, Haidian District, Beijing, China.
  • Yuqing Zhao
    Faculty of Mechanical and Electrical Engineering, Yunnan Agricultural University, Kunming, Yunnan 650201, PR China. Electronic address: kmyuqing@163.com.
  • Qizheng Wang
    Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China.
  • Lixiang Gao
    Department of Radiology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, People's Republic of China.
  • Chunyan Tian
    Department of Radiology, Peking University Third Hospital, Haidian District, Beijing, People's Republic of China. huishuy@bjmu.edu.cn.
  • Huishu Yuan
    Department of Radiology, Peking University Third Hospital, Beijing 10019, China.

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