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A deep-learning model for identifying fresh vertebral compression fractures on digital radiography

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To develop a deep-learning (DL) model for identifying fresh VCFs from digital radiography (DR), with magnetic resonance imaging (MRI) as the reference standard.

Methods

Patients with lumbar VCFs were retrospectively enrolled from January 2011 to May 2020. All patients underwent DR and MRI scanning. VCFs were categorized as fresh or old according to MRI results, and the VCF grade and type were assessed. The raw DR data were sent to InferScholar Center for annotation. A DL-based prediction model was built, and its diagnostic performance was evaluated. The DeLong test was applied to assess differences in ROC curves between different models.

Results

A total of 1877 VCFs in 1099 patients were included in our study and randomly divided into development (n = 824 patients) and test (n = 275 patients) datasets. The ensemble model identified fresh and old VCFs, reaching an AUC of 0.80 (95% confidence interval [CI], 0.77–0.83), an accuracy of 74% (95% CI, 72–77%), a sensitivity of 80% (95% CI, 77–83%), and a specificity of 68% (95% CI, 63–72%). Lateral (AUC, 0.83) views exhibited better performance than anteroposterior views (AUC, 0.77), and the best performance among respective subgroupings was obtained for grade 3 (AUC, 0.89) and crush-type (AUC, 0.87) subgroups.

Conclusion

The proposed DL model achieved adequate performance in identifying fresh VCFs from DR.

Key Points

• The ensemble deep-learning model identified fresh VCFs from DR, reaching an AUC of 0.80, an accuracy of 74%, a sensitivity of 80%, and a specificity of 68% with the reference standard of MRI.

• The lateral views (AUC, 0.83) exhibited better performance than anteroposterior views (AUC, 0.77).

• The grade 3 (AUC, 0.89) and crush-type (AUC, 0.87) subgroups showed the best performance among their respective subgroupings.

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Abbreviations

AI:

Artificial intelligence

AP:

Anteroposterior

AUC:

Area under the curve

BMEs:

Bone marrow edemas

CNN:

Convolutional neural network

DL:

Deep-learning

DR:

Digital radiography

LAT:

Lateral

MRI:

Magnetic resonance imaging

ROC:

Receiver operating characteristic

VCFs:

Vertebral compression fractures

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Acknowledgements

The authors are grateful to American Journal Experts (AJE) for their assistance with language editing.

Funding

This study was supported by the Kuanren Talents Program of the Second Affiliated Hospital of Chongqing Medical University (2020–7).

Author information

Authors and Affiliations

  1. Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, No. 74 Linjiang Rd, Yuzhong District, Chongqing, 400010, China

    Weijuan Chen, Xi Liu, Kunhua Li, Yin Luo, Shanwei Bai & Dajing Guo

  2. Department of Applied Clinical Medicine, Infervision, Beijing, China

    Jiangfen Wu, Weidao Chen & Mengxing Dong

Authors
  1. Weijuan Chen
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  2. Xi Liu
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  3. Kunhua Li
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Correspondence to Dajing Guo.

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Guarantor

The scientific guarantor of this publication is Dajing Guo.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Infervision.

Statistics and biometry

Infervision kindly provided statistical advice for this manuscript.

One of the authors has significant statistical expertise.

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Written informed consent was not required for this study because this was a retrospective study.

Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

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• retrospective

• cross-sectional study/diagnostic or prognostic study/observational

• performed at one institution

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Chen, W., Liu, X., Li, K. et al. A deep-learning model for identifying fresh vertebral compression fractures on digital radiography. Eur Radiol 32, 1496–1505 (2022). https://doi.org/10.1007/s00330-021-08247-4

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  • DOI: https://doi.org/10.1007/s00330-021-08247-4

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