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

Chi-Tung Cheng, Yirui Wang, Huan-Wu Chen, Po-Meng Hsiao, Chun-Nan Yeh, Chi-Hsun Hsieh, Shun Miao, Jing Xiao, Chien-Hung Liao () and Le Lu
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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
Le Lu: PAII Inc

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-21311-3

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