Abstract
Introduction and Objective
Up to 30% of thoracolumbar (TL) fractures are missed in the emergency room. Failure to identify these fractures can result in neurological injuries up to 51% of the casesthis article aimed to clarify the incidence and risk factors of traumatic fractures in China. The China National Fracture Study (CNFS. Obtaining sagittal and anteroposterior radiographs of the TL spine are the first diagnostic step when suspecting a traumatic injury. In most cases, CT and/or MRI are needed to confirm the diagnosis. These are time and resource consuming. Thus, reliably detecting vertebral fractures in simple radiographic projections would have a significant impact. We aim to develop and validate a deep learning tool capable of detecting TL fractures on lateral radiographs of the spine. The clinical implementation of this tool is anticipated to reduce the rate of missed vertebral fractures in emergency rooms.
Materials and Methods
We collected sagittal radiographs, CT and MRI scans of the TL spine of 362 patients exhibiting traumatic vertebral fractures. Cases were excluded when CT and/or MRI where not available. The reference standard was set by an expert group of three spine surgeons who conjointly annotated (fracture/no-fracture and AO Classification) the sagittal radiographs of 171 cases. CT and/or MRI were used confirm the presence and type of the fracture in all cases. 302 cropped vertebral images were labelled “fracture” and 328 “no fracture”. After augmentation, this dataset was then used to train, validate, and test deep learning classifiers based on the ResNet18 and VGG16 architectures. To ensure that the model's prediction was based on the correct identification of the fracture zone, an Activation Map analysis was conducted.
Results
Vertebras T12 to L2 were the most frequently involved, accounting for 48% of the fractures. Accuracies of 88% and 84% were obtained with ResNet18 and VGG16 respectively. The sensitivity was 89% with both architectures but ResNet18 had a significantly higher specificity (88%) compared to VGG16 (79%). The fracture zone used was precisely identified in 81% of the heatmaps.
Conclusions
Our AI model can accurately identify anomalies suggestive of TL vertebral fractures in sagittal radiographs precisely identifying the fracture zone within the vertebral body.
