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Automatic detection of early osteonecrosis of the femoral head from various hip pathologies using deep convolutional neural network: a multi-centre study

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Abstract

Purpose

The aim of this study was to develop a deep convolutional neural network (DCNN) for detecting early osteonecrosis of the femoral head (ONFH) from various hip pathologies and evaluate the feasibility of its application.

Methods

We retrospectively reviewed and annotated hip magnetic resonance imaging (MRI) of ONFH patients from four participated institutions and constructed a multi-centre dataset to develop the DCNN system. The diagnostic performance of the DCNN in the internal and external test datasets was calculated, including area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, and F1 score, and gradient-weighted class activation mapping (Grad-CAM) technique was used to visualize its decision-making process. In addition, a human–machine comparison trial was performed.

Results

Overall, 11,730 hip MRI segments from 794 participants were used to develop and optimize the DCNN system. The AUROC, accuracy, and precision of the DCNN in internal test dataset were 0.97 (95% CI, 0.93–1.00), 96.6% (95% CI: 93.0–100%), and 97.6% (95% CI: 94.6–100%), and in external test dataset, they were 0.95 (95% CI, 0.91– 0.99), 95.2% (95% CI, 91.1–99.4%), and 95.7% (95% CI, 91.7–99.7%). Compared with attending orthopaedic surgeons, the DCNN showed superior diagnostic performance. The Grad-CAM demonstrated that the DCNN placed focus on the necrotic region.

Conclusion

Compared with clinician-led diagnoses, the developed DCNN system is more accurate in diagnosing early ONFH, avoiding empirical dependence and inter-reader variability. Our findings support the integration of deep learning systems into real clinical settings to assist orthopaedic surgeons in diagnosing early ONFH.

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Data availability

All materials and data generated from this study are available upon request to the corresponding author.

Code availability

The code included in this study is available upon request to the corresponding author.

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Acknowledgements

We thank the review panel members in our study who reviewed the MRI scan data we included and the radiologists at the four participated institutions for their guidance and assistance.

Funding

This work was supported by funding from the Key Project of the National Natural Science Foundation of China (No.: U21A20390), Jilin Scientific and Technological Development Program (No.:20230203089SF), Natural Science Foundation of Jilin Province (No.: 20200201536JC), and the Project of Jilin Provincial Department of Finance (No.: 2020SCZ63).

Author information

Author notes

  1. Xianyue Shen and Ziling He contributed equally.

Authors and Affiliations

  1. Department of Orthopedics, The Second Hospital of Jilin University, Changchun, Jilin Province, People’s Republic of China

    Xianyue Shen, Xiongfeng Tang, Bo Chen & Yanguo Qin

  2. College of Computer Science and Technology, Jilin University, Changchun, Jilin Province, People’s Republic of China

    Ziling He & Jia Luo

  3. Department of Orthopedics, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People’s Republic of China

    Yi Shi

  4. Department of Orthopedics, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province, People’s Republic of China

    Yuhui Yang

  5. Department of Orthopedics, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, People’s Republic of China

    Tong Liu, Shenghao Xu & Jianlin Xiao

  6. College of Software, Jilin University, Changchun, Jilin Province, People’s Republic of China

    You Zhou

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Contributions

Concept and design: Shen, Xiao, and Qin.

Expert orthopaedic surgeons read: Xiao, Liu, and Qin.

Collection and curation of the clinical datasets: Shen, Shi, Yang, Chen, and Tang.

Development, training, validation, and optimization of convolutional neural networks: He, Luo, and Zhou.

Data analysis and interpretation: Shen, Xu, Chen, and Tang.

Drafting of the manuscript: Shen, Xu, Chen, and Tang.

Critical analysis and manuscript revision: all authors.

All the authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jianlin Xiao, You Zhou or Yanguo Qin.

Ethics declarations

Ethics approval

Institutional ethical board review was obtained for the present study (No: SB2021-012).

Consent to participate

Informed consent was waived because materials of included patients were anonymized and desensitized.

Consent for publication

Informed consent was waived because materials of included patients were anonymized and desensitized.

Competing interests

The authors declare no competing interests.

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Investigation performed at The Second Hospital of Jilin University, Changchun, China.

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Shen, X., He, Z., Shi, Y. et al. Automatic detection of early osteonecrosis of the femoral head from various hip pathologies using deep convolutional neural network: a multi-centre study. International Orthopaedics (SICOT) 47, 2235–2244 (2023). https://doi.org/10.1007/s00264-023-05813-x

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