Prediction of subsequent osteoporotic vertebral compression fracture on CT radiography via deep learning

Abstract

Abstract Combination of computed tomography (CT) radiography and deep learning to predict subsequent osteoporotic vertebral compression fracture (OVCF) has not been reported. To do so, we analyzed retrospectively CT images from 103 patients who experienced twice OVCF in Tongji Hospital from 2011 to 2022. Meanwhile, CT images from 70 age‐matched osteoporotic patients without vertebral fracture were used as the negative control. Convolutional neural network was used for classification and the Adam optimizer combining the momentum and exponentially weighted moving average gradients methods were used to update the weights of the networks. In the prediction model, we split 80% data of each type of the patient as the training group, while the other 20% was held as the independent testing group. We found that the number of subsequent fracture in women is higher than that in men (81 vs. 22). Additionally, the incidence rate of adjacent vertebral fracture is higher than that of remote vertebral fracture (64.1 vs. 35.9%), while the onset time of the former was 11.9 ± 12.8 months, significantly less than 22.3 ± 18.2 months of the latter (p < .001). For the prediction of subsequent fracture, our model attained .839 of accuracy and .883 of receiver operating characteristic–area under curve on the whole testing dataset. Furthermore, our model gained .867 and .719 of accuracy on the single‐class testing dataset separated from the former, .817 of accuracy on the independent test. In conclusion, we managed to generate a deep learning‐based model, which is able to predict subsequent OVCF in a precise and unbiased way just using CT images.

Keywords

• computed tomography
• convolutional neural network
• deep learning
• osteoporotic vertebral compression fracture
• prediction model