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

Aug 5, 2026

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Vertebroplasty treats osteoporotic vertebral compression fractures with a minimally invasive PMMA injection, delivering fast pain relief and generally low complication rates. A key unresolved issue is the notable incidence of subsequent vertebral fractures after treatment, with a substantial portion occurring in adjacent vertebrae that were previously augmented. This work builds machine-learning predictive models using pre-determined risk factors to estimate fracture likelihood at the adjacent level and evaluate model accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Abstract

Background

Purpose

Methods

Results

Conclusion

Introduction

Vertebral compression fractures and osteoporosis

Vertebroplasty and clinical rationale

Risk of adjacent vertebral fractures

Aalborg Universitet Machine learning applications for predicting fracture of the adjacent vertebra after vertebroplasty Hasanpour, Maede; Einafshar, Mohammadjavad (Matin); Haghpanahi, Mohammad; Massaad, Elie; Kiapour, Ali Published in: Intelligence-Based Medicine DOI (link to publication from Publisher): 10.1016/j.ibmed.2025.100205 Creative Commons License CC BY-NC-ND 4.0 Publication date: 2025 Document Version Publisher's PDF, also known as Version of record Link to publication from Aalborg University Citation for published version (APA): Hasanpour, M. , Einafshar, M. , Haghpanahi, M. , Massaad, E. , & Kiapour, A. (2025) . Machine learning applications for predicting fracture of the adjacent vertebra after vertebroplasty. Intelligence-Based Medicine, 11 , 1-7. Article 100205. [https://doi.org/10.1016/j.ibmed.2025.100205](https://doi.org/10.1016/j.ibmed.2025.100205) General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. -Users may download and print one copy of any publication from the public portal for the purpose of private study or research. -You may not further distribute the material or use it for any profit-making activity or commercial gain -You may freely distribute the URL identifying the publication in the public portal Take down policy If you believe that this document breaches copyright please contact [us at vbn@aub.aau.dk](us at vbn@aub.aau.dk) providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from [vbn.aau.dk](vbn.aau.dk) on: August 04, 2026 Intelligence-Based Medicine 11 (2025) 100205 Contents lists available at ScienceDirect Intelligence-Based Medicine journal [homepage:](homepage: www.sciencedirect.com/journal/intelligence-based-medicine)[ www.sciencedirect.com/journal/intelligence-based-medicine](homepage: www.sciencedirect.com/journal/intelligence-based-medicine) | Machine learning applications for predicting fracture of the adjacent vertebra after vertebroplasty
Maede Hasanpour a, Mohammadjavad (Matin) Einafshar b, Mohammad Haghpanahic, Elie Massaadd, Ali Kiapour d,*
a Department of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran b Department of Material and Production, Aalborg University, Aalborg, Denmark
c Department of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran d Department of Neurosurgery, Massachusetts General Hospital, Harvard, Medical School, Boston, MA, USA | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Keywords:
Vertebroplasty
Adjacent Vertebra fracture Compression fracture Machine learning Classification method | | 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 t

FAQs

Why is predicting adjacent vertebral fractures after vertebroplasty clinically important?

A meaningful share of subsequent fractures occurs in adjacent vertebrae after augmentation. Predictive identification can support preventive strategies and personalized follow-up care.

Which machine-learning algorithms were used to predict fractures at the adjacent level?

K-nearest neighbors (KNN), decision tree (DT), support vector machine (SVM), and logistic regression (LR) were used to build predictive models.

What factors were identified as key predictors in the study?

The decision tree highlighted bone mineral density (BMD), cement volume, and cement stiffness. Logistic regression emphasized BMD, cement volume, and cement location.

Aug 5, 2026

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