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A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs

Author

Listed:
  • Chi-Tung Cheng

    (Chang Gung University)

  • Yirui Wang

    (PAII Inc)

  • Huan-Wu Chen

    (Chang Gung University College of Medicine)

  • Po-Meng Hsiao

    (New Taipei Municipal TuCheng Hospital)

  • Chun-Nan Yeh

    (Chang Gung University)

  • Chi-Hsun Hsieh

    (Chang Gung University)

  • Shun Miao

    (PAII Inc)

  • Jing Xiao

    (PAII Inc)

  • Chien-Hung Liao

    (Chang Gung University
    Center for Artificial Intelligence in Medicine, Chang Gung Memorial hospital, Linkou)

  • Le Lu

    (PAII Inc)

Abstract

Pelvic radiograph (PXR) is essential for detecting proximal femur and pelvis injuries in trauma patients, which is also the key component for trauma survey. None of the currently available algorithms can accurately detect all kinds of trauma-related radiographic findings on PXRs. Here, we show a universal algorithm can detect most types of trauma-related radiographic findings on PXRs. We develop a multiscale deep learning algorithm called PelviXNet trained with 5204 PXRs with weakly supervised point annotation. PelviXNet yields an area under the receiver operating characteristic curve (AUROC) of 0.973 (95% CI, 0.960–0.983) and an area under the precision-recall curve (AUPRC) of 0.963 (95% CI, 0.948–0.974) in the clinical population test set of 1888 PXRs. The accuracy, sensitivity, and specificity at the cutoff value are 0.924 (95% CI, 0.912–0.936), 0.908 (95% CI, 0.885–0.908), and 0.932 (95% CI, 0.919–0.946), respectively. PelviXNet demonstrates comparable performance with radiologists and orthopedics in detecting pelvic and hip fractures.

Suggested Citation

  • Chi-Tung Cheng & Yirui Wang & Huan-Wu Chen & Po-Meng Hsiao & Chun-Nan Yeh & Chi-Hsun Hsieh & Shun Miao & Jing Xiao & Chien-Hung Liao & Le Lu, 2021. "A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21311-3
    DOI: 10.1038/s41467-021-21311-3
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