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Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis

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  • Jongyun Jung
  • Jingyuan Dai
  • Bowen Liu
  • Qing Wu

Abstract

Artificial Intelligence (AI), encompassing Machine Learning and Deep Learning, has increasingly been applied to fracture detection using diverse imaging modalities and data types. This systematic review and meta-analysis aimed to assess the efficacy of AI in detecting fractures through various imaging modalities and data types (image, tabular, or both) and to synthesize the existing evidence related to AI-based fracture detection. Peer-reviewed studies developing and validating AI for fracture detection were identified through searches in multiple electronic databases without time limitations. A hierarchical meta-analysis model was used to calculate pooled sensitivity and specificity. A diagnostic accuracy quality assessment was performed to evaluate bias and applicability. Of the 66 eligible studies, 54 identified fractures using imaging-related data, nine using tabular data, and three using both. Vertebral fractures were the most common outcome (n = 20), followed by hip fractures (n = 18). Hip fractures exhibited the highest pooled sensitivity (92%; 95% CI: 87–96, p

Suggested Citation

  • Jongyun Jung & Jingyuan Dai & Bowen Liu & Qing Wu, 2024. "Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis," PLOS Digital Health, Public Library of Science, vol. 3(1), pages 1-22, January.
    • Handle: RePEc:plo:pdig00:0000438
    DOI: 10.1371/journal.pdig.0000438
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