Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan.
PAII Inc, Bethesda, MD, USA.
Nat Commun. 2021 Feb 16;12(1):1066. doi: 10.1038/s41467-021-21311-3.
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.
骨盆射线照相（PXR）对于检测创伤患者的股骨近端和骨盆损伤至关重要，这也是创伤筛查的关键组成部分。目前尚无任何可用的算法可以准确检测 PXR 上的所有类型的与创伤相关的放射学发现。在这里，我们展示了一种通用算法，可以检测 PXR 上大多数类型的与创伤相关的放射学发现。我们开发了一种名为 PelviXNet 的多尺度深度学习算法，该算法使用带有弱监督点注释的 5204 张 PXR 进行训练。在 1888 张 PXR 的临床人群测试集中，PelviXNet 的接收者操作特征曲线下面积（AUROC）为 0.973（95%CI，0.960-0.983），精度-召回曲线下面积（AUPRC）为 0.963（95%CI，0.948-0.974）。在截止值处的准确率、敏感度和特异性分别为 0.924（95%CI，0.912-0.936）、0.908（95%CI，0.885-0.908）和 0.932（95%CI，0.919-0.946）。PelviXNet 在检测骨盆和髋部骨折方面的表现与放射科医生和骨科医生相当。
