Machine learning applications for predicting fracture of the adjacent vertebra after vertebroplasty

Background: Vertebroplasty, a minimally invasive procedure for treating vertebral compression fractures, has shown promising clinical outcomes due to its straightforward surgical technique, low complication rate, and rapid pain relief. However, a significant concern is the 25 % rate of subsequent vertebral fractures following treatment, with 50–67 % of these occurring in adjacent vertebrae that were previously augmented. Purpose: To develop predictive models for fractures in vertebrae adjacent to those treated with vertebroplasty using machine learning techniques and a classification method based on pre-determined risk factors. Methods: A retrospective study has been conducted to discover potential factors that could influence the effectiveness of vertebroplasty. Models were developed using data from 84 patients with osteoporotic vertebral compression fractures (OVCF) who underwent vertebroplasty. K-nearest neighbors (KNN), decision tree (DT), support vector machine (SVM), and logistic regression (LR) algorithms were used to predict fractures at the adjacent level of the augmented vertebra after vertebroplasty. The accuracies of the models were also reported. Results: The DT and LR models achieved an accuracy of 0.94, while KNN and SVM models had an accuracy of 0.88. The DT identified bone mineral density (BMD), cement volume, and cement stiffness as key predictive factors. In contrast, the LR determined BMD, cement volume, and cement location to be the most essential features. Furthermore, the DT and LR models demonstrated the highest macro-average and weighted average metrics, calculated as 0.92 and 0.95, respectively. Conclusion: The high accuracies achieved by the machine learning models confirm their effectiveness in predicting subsequent adjacent vertebral fractures (SAVF) following vertebroplasty. Utilizing these predictive models in clinical practice may enable the successful identification of patients at high risk for SAVF, potentially contributing to preventing these complications through personalized treatment planning and follow-up care.