Deep Learning-Based Prediction for Bone Cement Leakage During Percutaneous Kyphoplasty Using Preoperative Computed Tomography: MODEL Development and Validation.

Abstract

Study design: Retrospective study.

Objective: To develop a deep learning (DL) model to predict bone cement leakage (BCL) subtypes during percutaneous kyphoplasty (PKP) using preoperative computed tomography (CT) as well as employing multicenter data to evaluate the effectiveness and generalizability of the model.

Summary of background data: DL excels at automatically extracting features from medical images. However, there is a lack of models that can predict BCL subtypes based on preoperative images.

Methods: This study included an internal dataset for DL model training, validation, and testing as well as an external dataset for additional model testing. Our model integrated a segment localization module based on vertebral segmentation via three-dimensional (3D) U-Net with a classification module based on 3D ResNet-50. Vertebral level mismatch rates were calculated, and confusion matrixes were used to compare the performance of the DL model with that of spine surgeons in predicting BCL subtypes. Furthermore, the simple Cohen's kappa coefficient was used to assess the reliability of spine surgeons and the DL model against the reference standard.

Results: A total of 901 patients containing 997 eligible segments were included in the internal dataset. The model demonstrated a vertebral segment identification accuracy of 96.9%. It also showed high area under the curve (AUC) values of 0.734-0.831 and sensitivities of 0.649-0.900 for BCL prediction in the internal dataset. Similar favorable AUC values of 0.709-0.818 and sensitivities of 0.706-0.857 were observed in the external dataset, indicating the stability and generalizability of the model. Moreover, the model outperformed nonexpert spine surgeons in predicting BCL subtypes, except for type II.

Conclusion: The model achieved satisfactory accuracy, reliability, generalizability, and interpretability in predicting BCL subtypes, outperforming nonexpert spine surgeons. This study offers valuable insights for assessing osteoporotic vertebral compression fractures, thereby aiding preoperative surgical decision-making.

Level of evidence: 3.