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Application of artificial intelligence in diagnosis of osteoporosis using medical images: a systematic review and meta-analysis

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Abstract

Artificial intelligence (AI) is a potentially reliable assistant in the diagnosis of osteoporosis. This meta-analysis aims to assess the diagnostic accuracy of the AI-based systems using medical images. We searched PubMed and Web of Science from inception to June 15, 2020, for eligible articles that applied AI approaches to diagnosing osteoporosis using medical images. Quality and bias of the included studies were evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The main outcome was the sensitivity and specificity of the performance of the AI-based systems. The data analysis utilized the R Foundation packages of “meta” for univariate analysis and Stata for bivariate analysis. Random effects model was utilized. Seven studies with 3186 patients were included in the meta-analysis. The overall risk of bias of the included studies was assessed as low. The pooled sensitivity was 0.96 (95% CI 0.93–1.00), and the pooled specificity was 0.95 (95% CI 0.91–0.99). However, high heterogeneity was found in this meta-analysis. The results supported that the AI-based systems had good accuracy in diagnosing osteoporosis. However, the high risk of bias in patient selection and high heterogeneity in the meta-analysis made the conclusion less convincing. The application of AI-based systems in osteoporosis diagnosis needs to be further confirmed by more prospective studies in multi-centers including more random samples from complete patient types.

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Data availability

All data are included in this article and its supplementary file.

Abbreviations

AI:

artificial intelligence

AUC:

area under the receiver operating characteristic curve

CI:

confidence intervals

CNN:

convolutional neural network

FP:

false positive

FN:

false negative

N:

negative

P:

positive

QUADAS-2:

quality assessment of diagnostic accuracy studies tool

HSROC:

hierarchical summary receiver-operating characteristic curve

TP:

true positive

TN:

true negative

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Acknowledgement

The authors thank Tiago V. Pereira for revision of the article that greatly improved the manuscript.

Funding

This study was supported by China Postdoctoral Science Foundation (Grant No. 2019M650598).

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Authors and Affiliations

  1. Beijing University of Chinese Medicine, Beijing, 100029, China

    L. Gao, T. Jiao, Q. Feng & W. Wang

  2. Applied Health Research Centre (AHRC), Li Ka Shing Knowledge Institute, St Michael’s Hospital, University of Toronto, Toronto, M5B 1W8, Canada

    L. Gao

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  1. L. Gao
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  2. T. Jiao
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  3. Q. Feng
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  4. W. Wang
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Contributions

Conception and design, Gao L and Wang W; Analysis and interpretation of the data: Gao L, Jiao T, Feng Q; Drafting of the article: Gao L; Critical revision of the article for important intellectual content: Gao L and Wang W; Final approval of the article: all the authors.

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Correspondence to L. Gao or W. Wang.

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Gao, L., Jiao, T., Feng, Q. et al. Application of artificial intelligence in diagnosis of osteoporosis using medical images: a systematic review and meta-analysis. Osteoporos Int 32, 1279–1286 (2021). https://doi.org/10.1007/s00198-021-05887-6

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  • DOI: https://doi.org/10.1007/s00198-021-05887-6

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