ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2023
Machine-learning-based approach for nonunion prediction following osteoporotic vertebral fractures.
Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of prediction models for delayed union and nonunion after fracture: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2026Pooled it
- What Factors Contribute to the Poor Prognosis of Conservative Treatment for Osteoporotic Vertebral Compression Fracture (OVCF): A Systematic Review.Clinical interventions in aging · 2025Pooled it
- Predicting nonunion - a hybrid machine-learning model combining patient factors and mechanobiological simulation features exceeds clinician accuracy.Bone & joint research · 2026Article
- The Elusive Concept of Stability in Osteoporotic Vertebral Fractures: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Article
- Opportunistic osteoporosis screening using CT-based Hounsfield units: a machine learning approach for preoperative spine surgery planning.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- AI-driven Technologies for Wrist Fracture Prediction: A Narrative Review of Emerging Approaches.Journal of wrist surgery · 2025Article
- Artificial Intelligence in Spine Surgery: Imaging-Based Applications for Diagnosis and Surgical Techniques.Current reviews in musculoskeletal medicine · 2025Review
- Machine learning can predict surgical indication: new clustering model from a large adult spine deformity database.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Article
- Innovative machine learning approach for liver fibrosis and disease severity evaluation in MAFLD patients using MRI fat content analysis.Clinical and experimental medicine · 2025Article
- Deep Learning in Scaphoid Nonunion Treatment.Journal of clinical medicine · 2025Article
- A Retrospective Machine Learning Analysis to Predict 3-Month Nonunion of Unstable Distal Clavicle Fracture Patients Treated with Open Reduction and Internal Fixation.Therapeutics and clinical risk management · 2025Article
- Development and reporting of artificial intelligence in osteoporosis management.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2024Review
- Machine Learning and Deep Learning in Spinal Injury: A Narrative Review of Algorithms in Diagnosis and Prognosis.Journal of clinical medicine · 2024Review
- Management of Osteoporotic Vertebral Fracture: Review Update 2022.Asian spine journal · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAn osteoporotic vertebral fracture (OVF) is a common disease that causes disabilities in elderly patients. In particular, patients with nonunion following an OVF often experience severe back pain and require surgical intervention. However, nonunion diagnosis generally takes more than six months. Although several studies have advocated the use of magnetic resonance imaging (MRI) observations as predictive factors, they exhibit insufficient accuracy. The purpose of this study was to create a predictive model for OVF nonunion using machine learning (ML).
methodsWe used datasets from two prospective cohort studies for OVF nonunion prediction based on conservative treatment. Among 573 patients with acute OVFs exceeding 65 years in age enrolled in this study, 505 were analyzed. The demographic data, fracture type, and MRI observations of both studies were analyzed using ML. The ML architecture utilized in this study included a logistic regression model, decision tree, extreme gradient boosting (XGBoost), and random forest (RF). The datasets were processed using Python.
resultsThe two ML algorithms, XGBoost and RF, exhibited higher area under the receiver operating characteristic curves (AUCs) than the logistic regression and decision tree models (AUC = 0.860 and 0.845 for RF and XGBoost, respectively). The present study found that MRI findings, anterior height ratio, kyphotic angle, BMI, VAS, age, posterior wall injury, fracture level, and smoking habit ranked as important features in the ML algorithms.
conclusionML-based algorithms might be more effective than conventional methods for nonunion prediction following OVFs.
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Registered trials
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.