A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs.
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ABSTRACT:
SUBMITTER: Cheng CT
PROVIDER: S-EPMC7887334 | biostudies-literature | 2021 Feb
REPOSITORIES: biostudies-literature
Publications
A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs.
Cheng Chi-Tung CT Wang Yirui Y Chen Huan-Wu HW Hsiao Po-Meng PM Yeh Chun-Nan CN Hsieh Chi-Hsun CH Miao Shun S Xiao Jing J Liao Chien-Hung CH Lu Le L
Nature communications 20210216 1
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 p ...[more]
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