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Diagnostic Accuracy of Artificial Intelligence-Based Algorithms in Automated Detection of Neck of Femur Fracture on a Plain Radiograph: A Systematic Review and Meta-analysis

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Abstract

Objectives

To evaluate the diagnostic accuracy of artificial intelligence-based algorithms in identifying neck of femur fracture on a plain radiograph.

Design

Systematic review and meta-analysis.

Data sources

PubMed, Web of science, Scopus, IEEE, and the Science direct databases were searched from inception to 30 July 2023.

Eligibility criteria for study selection

Eligible article types were descriptive, analytical, or trial studies published in the English language providing data on the utility of artificial intelligence (AI) based algorithms in the detection of the neck of the femur (NOF) fracture on plain X-ray.

Main outcome measures

The prespecified primary outcome was to calculate the sensitivity, specificity, accuracy, Youden index, and positive and negative likelihood ratios. Two teams of reviewers (each consisting of two members) extracted the data from available information in each study. The risk of bias was assessed using a mix of the CLAIM (the Checklist for AI in Medical Imaging) and QUADAS-2 (A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies) criteria.

Results

Of the 437 articles retrieved, five were eligible for inclusion, and the pooled sensitivity of AIs in diagnosing the fracture NOF was 85%, with a specificity of 87%. For all studies, the pooled Youden index (YI) was 0.73. The average positive likelihood ratio (PLR) was 19.88, whereas the negative likelihood ratio (NLR) was 0.17. The random effects model showed an overall odds of 1.16 (0.84–1.61) in the forest plot, comparing the AI system with those of human diagnosis. The overall heterogeneity of the studies was marginal (I2 = 51%). The CLAIM criteria for risk of bias assessment had an overall >70% score.

Conclusion

Artificial intelligence (AI)-based algorithms can be used as a diagnostic adjunct, benefiting clinicians by taking less time and effort in neck of the femur (NOF) fracture diagnosis.

Study registration

PROSPERO CRD42022375449.

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Authors and Affiliations

  1. Department of Orthopaedic, All India Institute of Medical Sciences, Deoghar, Jharkhand, India

    Manish Raj

  2. Department of Community and Family Medicine, All India Institute of Medical Sciences, Deoghar, Jharkhand, India

    Arshad Ayub

  3. Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India

    Arup Kumar Pal

  4. Department of Computer Science and Engineering, National Institute of Technology (NIT), Jamshedpur, Jharkhand, India

    Jitesh Pradhan

  5. Department of Computer Science and Engineering, GITAM University, Hyderabad Campus, Telangana, India

    Naushad Varish

  6. Informatics Cluster, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Sumit Kumar

  7. Department of Biochemistry, All India Institute of Medical Sciences, Deoghar, Jharkhand, India

    Seshadri Reddy Varikasuvu

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Raj, M., Ayub, A., Pal, A.K. et al. Diagnostic Accuracy of Artificial Intelligence-Based Algorithms in Automated Detection of Neck of Femur Fracture on a Plain Radiograph: A Systematic Review and Meta-analysis. JOIO 58, 457–469 (2024). https://doi.org/10.1007/s43465-024-01130-6

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