ArticleWorld journal of orthopedics2025
Accuracy of artificial intelligence in prediction of osteoporotic fractures in comparison with dual-energy X-ray absorptiometry and the Fracture Risk Assessment Tool: A systematic review.
Article in World journal of orthopedics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- The Elusive Concept of Stability in Osteoporotic Vertebral Fractures: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Development and validation of a multimodal interpretable machine learning model for the identification of osteoporosis in patients with type 2 diabetes mellitus: a multicenter retrospective study.Frontiers in endocrinology · 2026Article
- Development and validation of an interpretable machine learning model for osteoporosis prediction using routine blood tests: a retrospective cohort study.BMC medical informatics and decision making · 2025Article
- Development and validation of an explainable machine learning model for predicting osteoporosis in patients with type 2 diabetes mellitus.Frontiers in endocrinology · 2025Article
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7 authors.
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Abstract
backgroundOsteoporotic fractures, whether due to postmenopausal or senile causes, impose a significant financial burden on developing countries and diminish quality of life. Recent advancements in artificial intelligence (AI) algorithms have demonstrated immense potential in predicting osteoporotic fractures.
aimTo assess and compare the efficacy of AI models against dual-energy X-ray absorptiometry (DXA) and the Fracture Risk Assessment Tool (FRAX) in predicting fragility fractures.
methodsWe conducted a literature search in English using electronic databases, including PubMed, Web of Science, and Scopus, for studies published until May 2024. The keywords employed were fragility fractures, osteoporosis, AI, deep learning, machine learning, and convolutional neural network. The inclusion criteria for selecting publications were based on studies involving patients with proximal femur and vertebral column fractures due to osteoporosis, utilizing AI algorithms, and analyzing the site of fracture and accuracy for predicting fracture risk using SPSS version 29 (Chicago, IL, United States).
resultsWe identified 156 publications for analysis. After applying our inclusion criteria, 24489 patients were analyzed from 13 studies. The mean area under the receiver operating characteristic curve was 0.925 ± 0.69. The mean sensitivity was 68.3% ± 15.3%, specificity was 85.5% ± 13.4%, and positive predictive value was 86.5% ± 6.3%. DXA showed a sensitivity of 37.0% and 74.0%, while FRAX demonstrated a sensitivity of 45.7% and 84.7%. The
conclusionThis review found that AI is a valuable tool to analyze and identify patients who will suffer from fragility fractures before they occur, demonstrating superiority over DXA and FRAX. Further studies are necessary to be conducted across various centers with diverse population groups, larger datasets, and a longer duration of follow-up to enhance the predictive performance of the AI models before their universal application.
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