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

Abstract

BACKGROUND: Osteoporotic 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. AIM: To 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. METHODS: We 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). RESULTS: value for sensitivity between DXA and AI was < 0.0001, while for FRAX it was < 0.0001 and 0.2. CONCLUSION: This 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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