ArticlePLOS digital health2024
Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis.
Article in PLOS digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 5 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed, 5 syntheses or guidelines pooled it, 53 citations in OpenAlex.
- The diagnostic accuracy of artificial intelligence in detecting foot and ankle fractures: a systematic review and meta-analysis of diagnostic test accuracy.Journal of orthopaedic surgery and research · 2026Pooled it
- Artificial intelligence for fracture detection on computed tomography: a comprehensive systematic review and meta-analysis of diagnostic test accuracy in non-commercial and commercial solutions.Emergency radiology · 2026Pooled it
- Diagnostic performance of artificial intelligence for facial fracture detection: a systematic review.Oral radiology · 2026Pooled it
- Artificial intelligence in virtual fracture clinics: a systematic review of imaging and clinical-text tools.Journal of orthopaedic surgery and research · 2026Pooled it
- Artificial Intelligence-Guided Assessment of Femoral Neck Fractures in Radiographs: A Systematic Review and Multilevel Meta-Analysis.Orthopaedic surgery · 2025Pooled it
- Performance, Heterogeneity, and Methodological Quality of YOLO-Based Models for Fracture Detection: A Systematic Review and Meta-Analysis.Journal of imaging informatics in medicine · 2026Review
- Diagnostic accuracy & clinical importance of AI confidence for extremity fracture detection: 2,508-patient retrospective cohort.Emergency radiology · 2026Article
- Attention-Guided EfficientNet-B3 with Grad-CAM Visualization for 22-Class Bone Fracture and Anatomical-Region Classification on the MultiBoneX Dataset.Diagnostics (Basel, Switzerland) · 2026Article
- Enhanced YOLOv8 with Hybrid Attention Gating Block for Intertrochanteric Fracture Detection in Complex X-Ray Images.Journal of imaging informatics in medicine · 2026Article
- Temporal Reproducibility of Fracture Interpretation in Forensic Radiography: A Multispecialty Comparison of Physicians and Vision Language Models Including Fracture Subtype Description.Tomography (Ann Arbor, Mich.) · 2026Article
- Osteoporosis: The Renascent Impact of Vertebral Fractures-A Narrative Review of Diagnosis, Risk Stratification, and Integrated Management.Journal of clinical medicine · 2026Review
- ChatGPT in Orthopedic Trauma: Consistency, Accuracy, and Agreement With Textbook and Expert Opinion.Cureus · 2026Article
- Clinical implementation of AI for vertebral fracture detection in CT aligned with fracture liaison services: high prevalence of undiagnosed vertebral fractures.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Observational
- FAD-MIL: a weakly supervised fracture detection model based on X-ray images.Scientific reports · 2026Article
- Orthopedic Trauma Management: A Comprehensive Review of Surgical Interventions, Rehabilitation, and Patient Outcomes.Cureus · 2026Review
- Deep learning and hybrid architectures for atypical and complex bone fracture diagnosis: a systematic review of performance and clinical validity.Frontiers in artificial intelligence · 2026Review
- A deep learning framework for automated fracture detection and localization in radiographic images using convolutional neural networks.Frontiers in medicine · 2026Article
- Performance of ChatGPT-5 in Diagnosing Fractures on Proximal Humerus and Intertrochanteric Femur X-Rays.Journal of orthopaedic case reports · 2026Article
- Artificial Intelligence in Bone Fracture Detection: A Review of Evidence, Limitations, and Clinical Integration.Cureus · 2025Review
- [Radiological imaging of spinal trauma in special situations].Radiologie (Heidelberg, Germany) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
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).
Identifiers
What OpenQuestion holds
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.