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Artificial intelligence in predicting postoperative heterotopic ossification following anterior cervical disc replacement

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Abstract

Objective

This study aimed to develop and validate a machine learning (ML) model to predict high-grade heterotopic ossification (HO) following Anterior cervical disc replacement (ACDR).

Methods

Retrospective review of prospectively collected data of patients undergoing ACDR or hybrid surgery (HS) at a quaternary referral medical center was performed. Patients diagnosed as C3-7 single- or multi-level cervical disc degeneration disease with > 2 years of follow-up and complete pre- and postoperative radiological imaging were included. An ML-based algorithm was developed to predict high grade HO based on perioperative demographic, clinical, and radiographic parameters. Furthermore, model performance was evaluated according to discrimination and overall performance.

Results

In total, 339 ACDR segments were included (61.65% female, mean age 45.65 ± 8.03 years). Over 45.65 ± 8.03 months of follow-up, 48 (14.16%) segments developed high grade HO. The model demonstrated good discrimination and overall performance according to precision (High grade HO: 0.71 ± 0.01, none-low grade HO: 0.85 ± 0.02), recall (High grade HO: 0.68 ± 0.03, none-low grade HO: 0.87 ± 0.01), F1-score (High grade HO: 0.69 ± 0.02, none-low grade HO: 0.86 ± 0.01), and AUC (0.78 ± 0.08), with lower prosthesis‑endplate depth ratio, higher height change, male, and lower postoperative-shell ROM identified as the most important predictive features.

Conclusion

Through an ML approach, the model identified risk factors and predicted development of high grade HO following ACDR with good discrimination and overall performance. By addressing the shortcomings of traditional statistics and adopting a new logical approach, ML techniques can support discovery, clinical decision-making, and intraoperative techniques better.

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Data availability

Datasets are available from the corresponding author on reasonable request.

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Acknowledgements

We are very grateful to those who helped in the study process.

Funding

This study was supported by the Cadre Health Research Project of Sichuan Province(ZH2023-105), National Natural Science Foundation of China (82302785), and the 1.3.5 project for Postdoctoral Foundation of West China Hospital of Sichuan University (2023HXBH080). No relevant financial activities outside of the submitted work.

Author information

Authors and Affiliations

  1. Department of Orthopaedic Surgery, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, Sichuan, 610041, China

    Rui Zong, Can Guo, Jun-bo He, Ting-kui Wu & Hao Liu

Authors
  1. Rui Zong
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  2. Can Guo
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  3. Jun-bo He
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  4. Ting-kui Wu
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  5. Hao Liu
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Contributions

RZ conducted the design of the study and drafted the manuscript with the help from CG and JBH. TKW and CG helped in the statistical analysis. RZ, CG, and JBH conducted the interpretation of data. TKW and HL contributed to the revision. The authors have read and approved the final manuscript.

Corresponding author

Correspondence to Hao Liu.

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Ethical approval

All participants provided signed, informed consent prior to study participation. This study was permitted by Ethics Committee on Biomedical Research, West China Hospital of Sichuan University (2019,946) and in accordance with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Consent for publication

Informed consent was obtained from all individual participants included in this study.

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The authors declare they have no competing interests.

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Zong, R., Guo, C., He, Jb. et al. Artificial intelligence in predicting postoperative heterotopic ossification following anterior cervical disc replacement. Eur Spine J (2024). https://doi.org/10.1007/s00586-024-08396-2

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