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- Title
Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis.
- Authors
Jung, Jongyun; Dai, Jingyuan; Liu, Bowen; Wu, Qing
- 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< 0.01) and specificity (90%; 95% CI: 85–93, p< 0.01). Pooled sensitivity and specificity using image data (92%; 95% CI: 90–94, p< 0.01; and 91%; 95% CI: 88–93, p < 0.01) were higher than those using tabular data (81%; 95% CI: 77–85, p< 0.01; and 83%; 95% CI: 76–88, p < 0.01), respectively. Radiographs demonstrated the highest pooled sensitivity (94%; 95% CI: 90–96, p < 0.01) and specificity (92%; 95% CI: 89–94, p< 0.01). Patient selection and reference standards were major concerns in assessing diagnostic accuracy for bias and applicability. AI displays high diagnostic accuracy for various fracture outcomes, indicating potential utility in healthcare systems for fracture diagnosis. However, enhanced transparency in reporting and adherence to standardized guidelines are necessary to improve the clinical applicability of AI. Review Registration: PROSPERO (CRD42021240359). Author summary: Artificial Intelligence (AI) is increasingly employed to detect fractures by using various imaging modalities and data types. Our search of Medline (via PubMed), Web of Science, and IEEE revealed numerous primary studies demonstrating AI's superior performance in fracture detection. This systematic review and meta-analysis is the first to assess and compare the diagnostic accuracy of AI models across different imaging modalities and data types for various fracture outcomes. We found that AI models achieve high accuracy in fracture detection, particularly with radiograph images. However, we identified significant flaws in study design and reporting, limiting real-world applicability. Few studies provided patient characteristics, and only half reported the hyperparameter selection process. Our findings underscore the benefits of using AI models with radiographs for fracture detection, as they outperform other imaging modalities. Despite similar results across modalities, inadequate methodology and reporting in AI model evaluations call for improvement. Considering AI's high diagnostic performance, integrating it into existing fracture risk assessment tools could enhance patient identification and enable early intervention.
- Subjects
DIAGNOSIS of bone fractures; COMPUTERS in medicine; ONLINE information services; META-analysis; ELECTRONIC data interchange; CONFIDENCE intervals; RESEARCH evaluation; SYSTEMATIC reviews; PATIENT selection; ARTIFICIAL intelligence; HIP fractures; MAGNETIC resonance imaging; DIAGNOSTIC imaging; QUALITY assurance; DESCRIPTIVE statistics; RESEARCH funding; SENSITIVITY &; specificity (Statistics); MEDLINE; COMPUTED tomography; COMPUTER-aided diagnosis; DIGITAL diagnostic imaging; VERTEBRAL fractures; EVALUATION
- Publication
PLoS Digital Health, 2024, Vol 3, Issue 1, p1
- ISSN
2767-3170
- Publication type
Article
- DOI
10.1371/journal.pdig.0000438