ArticleBMC surgery2023
Application of machine learning in prediction of bone cement leakage during single-level thoracolumbar percutaneous vertebroplasty.
Article in BMC surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 4 citations in OpenAlex.
- A Deep Learning-Based Multimodal Fusion Model for Predicting Bone Cement Leakage in Percutaneous Kyphoplasty: Development and Validation.JOR spine · 2026Article
- Development and validation of a predictive model for puncture accuracy in robot-assisted percutaneous vertebroplasty under local anesthesia.Journal of robotic surgery · 2026Article
- Development and validation of a nomogram model for predicting bone cement leakage during vertebroplasty.BMC surgery · 2026Article
- Comparative performance of LLMs and machine learning in predicting complications after percutaneous kyphoplasty for osteoporotic vertebral compression fractures.NPJ digital medicine · 2026Article
- Interpretable Machine Learning Model to Predict Bone Cement Leakage in Percutaneous Vertebral Augmentation for Osteoporotic Vertebral Compression Fracture Based on SHapley Additive exPlanations.Global spine journal · 2025Article
- Deep learning-based multimodal image analysis predicts bone cement leakage during percutaneous kyphoplasty: protocol for model development, and validation by prospective and external datasets.Frontiers in medicine · 2024Article
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Authors and funding
26 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundIn the elderly, osteoporotic vertebral compression fractures (OVCFs) of the thoracolumbar vertebra are common, and percutaneous vertebroplasty (PVP) is a common surgical method after fracture. Machine learning (ML) was used in this study to assist clinicians in preventing bone cement leakage during PVP surgery.
methodsThe clinical data of 374 patients with thoracolumbar OVCFs who underwent single-level PVP at The First People's Hospital of Chenzhou were chosen. It included 150 patients with bone cement leakage and 224 patients without it. We screened the feature variables using four ML methods and used the intersection to generate the prediction model. In addition, predictive models were used in the validation cohort.
resultsThe ML method was used to select five factors to create a Nomogram diagnostic model. The nomogram model's AUC was 0.646667, and its C value was 0.647. The calibration curves revealed a consistent relationship between nomogram predictions and actual probabilities. In 91 randomized samples, the AUC of this nomogram model was 0.7555116.
conclusionIn this study, we invented a prediction model for bone cement leakage in single-segment PVP surgery, which can help doctors in performing better surgery with reduced risk.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.