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Sensor and machine learning–based assessment of gap balancing in cadaveric unicompartmental knee arthroplasty surgical training

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Abstract

Purpose

The aim of this study was to assess the difference between flexion and extension contact forces—gap balance—after Oxford mobile-bearing medial unicompartmental knee arthroplasty (UKA) performed by surgeons with varying levels of experience.

Methods

Surgeons in a training programme performed UKAs on fresh frozen cadaveric specimens (n = 60). Contact force in the medial compartment of the knee was measured after UKA during extension and flexion using a force sensor, and values were clustered using an unsupervised machine learning (k-means algorithm). Univariate analysis was performed with general linear regression models to identify the explanatory variable.

Results

The level of experience was predictive of gap balance; surgeons were clustered into beginner, mid-level and experienced groups. Experienced surgeons’ mean difference between flexion and extension contact force was 83 N, which was significantly lower (p < 0.05) than that achieved by mid-level (215 N) or beginner (346 N) surgeons.

Conclusion

We found that the lowest mean difference between flexion and extension contact force after UKA was 83 N, which was achieved by surgeons with the most experience; this value can be considered the optimal value. Beginner and mid-level surgeons achieved values that were significantly lower. This study also demonstrates that machine learning can be used in combination with sensor technology for improving gap balancing judgement in UKA.

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Data availability

The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.

Code availability

The software and custom code of the current study are available from the corresponding author and author Chen Yang on reasonable request.

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Acknowledgements

We thank Kelly Zammit, BVSc, and Coren Walters-Stewart, PhD, from Liwen Bianji, Edanz Editing China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript. We thank Zimmer Biomet for providing the instruments in the cadaveric surgical training programme.

Funding

This study was funded by Beijing Municipal Science and Technology Commission (grant number Z171100001017209), the National Natural Science Foundation of China (grant numbers 81972130, 81703896, 81972107, 81902203, 82072494), National Key Research and Development Program of China (grant number 2017YFC0108102) and the Capital Health Research and Development of Special (grant number 2020–2-4067).

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Authors and Affiliations

  1. Graduate School of Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China

    Xiaowei Sun, Qidong Zhang, Nianfei Zhang, Weiguo Wang & Wanshou Guo

  2. Department of Orthopaedic Surgery, China-Japan Friendship Hospital, Beijing, China

    Xiaowei Sun, Qidong Zhang, Nianfei Zhang, Weiguo Wang & Wanshou Guo

  3. Department of Orthopaedic Surgery, University Paris East (UPEC), Hôpital Henri Mondor, Creteil, France

    Philippe Hernigou

  4. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Yang Chen

Authors
  1. Xiaowei Sun
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  2. Philippe Hernigou
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  3. Qidong Zhang
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  4. Nianfei Zhang
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  5. Weiguo Wang
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  6. Yang Chen
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  7. Wanshou Guo
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Contributions

Xiaowei Sun and Wanshou Guo contributed to the conception and design. Xiaowei Sun and Yang Chen designed and produced the force sensor. Xiaowei Sun and Philippe Hernigou did the analysis and interpretation. Qidong Zhang and Weiguo Wang did the data collection. Xiaowei Sun wrote the article. Philippe Hernigou, Nianfei Zhang and Wanshou Guo did the revision of the article.

Corresponding author

Correspondence to Wanshou Guo.

Ethics declarations

Ethics approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the ethics committee of the China-Japan Friendship Hospital, which was also the initiator of this series of UKA cadaveric surgical training programme. The approved number was 2020–50-k28.

Consent to participate

Informed consent was obtained from all training participants included in the study.

Consent for publication

The surgeons who participate in the training programme had consented to the submission of the report to the journal.

Conflict of interest

The authors declare no competing interests.

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Sun, X., Hernigou, P., Zhang, Q. et al. Sensor and machine learning–based assessment of gap balancing in cadaveric unicompartmental knee arthroplasty surgical training. International Orthopaedics (SICOT) 45, 2843–2849 (2021). https://doi.org/10.1007/s00264-021-05176-1

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