An Automated Deep Learning Method for Tile AO/OTA Pelvic Fracture Severity Grading from Trauma whole-Body CT

Abstract

Admission trauma whole-body CT is routinely employed as a first-line diagnostic tool for characterizing pelvic fracture severity. Tile AO/OTA grade based on the presence or absence of rotational and translational instability corresponds with need for interventions including massive transfusion and angioembolization. An automated method could be highly beneficial for point of care triage in this critical time-sensitive setting. A dataset of 373 trauma whole-body CTs collected from two busy level 1 trauma centers with consensus Tile AO/OTA grading by three trauma radiologists was used to train and test a triplanar parallel concatenated network incorporating orthogonal full-thickness multiplanar reformat (MPR) views as input with a ResNeXt-50 backbone. Input pelvic images were first derived using an automated registration and cropping technique. Performance of the network for classification of rotational and translational instability was compared with that of (1) an analogous triplanar architecture incorporating an LSTM RNN network, (2) a previously described 3D autoencoder-based method, and (3) grading by a fourth independent blinded radiologist with trauma expertise. Confusion matrix results were derived, anchored to peak Matthews correlation coefficient (MCC). Associations with clinical outcomes were determined using Fisher's exact test. The triplanar parallel concatenated method had the highest accuracies for discriminating translational and rotational instability (85% and 74%, respectively), with specificity, recall, and F1 score of 93.4%, 56.5%, and 0.63 for translational instability and 71.7%, 75.7%, and 0.77 for rotational instability. Accuracy of this method was equivalent to the single radiologist read for rotational instability (74.0% versus 76.7%, p = 0.40), but significantly higher for translational instability (85.0% versus 75.1, p = 0.0007). Mean inference time was < 0.1 s per test image. Translational instability determined with this method was associated with need for angioembolization and massive transfusion (p = 0.002-0.008). Saliency maps demonstrated that the network focused on the sacroiliac complex and pubic symphysis, in keeping with the AO/OTA grading paradigm. A multiview concatenated deep network leveraging 3D information from orthogonal thick-MPR images predicted rotationally and translationally unstable pelvic fractures with accuracy comparable to an independent reader with trauma radiology expertise. Model output demonstrated significant association with key clinical outcomes.

Keywords: Convolutional neural network; Deep learning; Pelvic fracture; Pelvic instability; Pelvic ring disruption; Tile classification.

Fig. 1

a Illustrates anteriorly divergent sacroiliac diastasis hinged about an intact posterior SI ligament. Rotational but not translational instability is the hallmark of a Type B Tile AO/OTA injury. This is associated with pubic symphysis widening in b (same patient). Pubic symphysis widening can be seen in both Type B and C injuries and is not as discriminative as the bony relationships about the SI joint. c–f are Type C injuries. In c and d, there is vertical translational displacement of the right innominate bone with respect to the sacrum. e Illustrates parallel widening of the SI joint characteristic of posterior SI ligament disruption. f Illustrates a variant where the juxta-articular sacrum is widely diastatic. In this instance the posterior SI ligament is intact, but functionally incompetent, with posterior translation of the left hemipelvis

Fig. 2

Flow diagram of automated deep learning method for instability prediction and Tile AO/OTA grading. a Automated partitioning of the pelvis from the whole-body CT. A bounding cube is created around the pelvis following NCC-based registration, and b the pelvic region is cropped. c Full thickness bone-window MPRs are generated from the cropped pelvic CT in the three orthogonal planes. d These are passed down three parallel ResNeXt-50 networks which are joined by a fully connected “view-pooling” layer that synthesizes information from all three views during discriminative learning and inference. e The network predicts whether rotational or translational instability are present or absent. The Tile AO/OTA grade is then determined. If there is no instability of either form, the fracture is Type A. If rotational instability is present but not vertical instability, the fracture is Type B. If translational instability is present, the fracture is type C

Fig. 3

Comparison between ResNet and ResNeXt backbone building blocks. Figure modified from Xie et al. (54). ResNet is shown left, ResNeXt shown right. Both use shortcut residual connections that allow very deep networks by alleviating vanishing and exploding gradients. ResNeXt (right) uses multiple reduced dimensionality blocks in a split-transform-merge strategy. The parallelization of building blocks is referred to as cardinality. The method improved performance over ResNet at ILSVRC 2016 and achieved second place for classification tasks

Fig. 4

Block flow diagram of recurrent neural network using ResNext-50 backbone. We implemented LSTM cells with three layers and a hidden size of 2048

Fig. 5

Activation maps are shown in three views: a anteroposterior, b axial, and c sagittal for a correctly classified Tile C patient using the concatenated triplanar ResNeXt architecture. In figure part a, attention is primarily focused on the SI joint and posterior pelvic ring of the superiorly translated right hemipelvis. In figure part b, attention is maximally focused centrally about the SI joints as well as the pubic symphysis which demonstrates AP translation. Maximal attention is focused on the abnormal pubic symphysis in figure part c