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Machine learning algorithms predict within one size of the final implant ultimately used in total knee arthroplasty with good-to-excellent accuracy

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Knee Surgery, Sports Traumatology, Arthroscopy Aims and scope

Abstract

Purpose

To develop a novel machine learning algorithm capable of predicting TKA implant sizes using a large, multicenter database.

Methods

A consecutive series of primary TKA patients from two independent large academic and three community medical centers between 2012 and 2020 was identified. The primary outcomes were final tibial and femoral implant sizes obtained from an automated inventory system. Five machine learning algorithms were trained using six routinely collected preoperative features (age, sex, height, weight, and body mass index). Algorithms were validated on an independent set of patients and evaluated through accuracy, mean absolute error (MAE), and root mean-squared error (RMSE).

Results

A total of 11,777 patients were included. The support vector machine (SVM) algorithm had the best performance for femoral component size(MAE = 0.73, RMSE = 1.06) with accuracies of 42.2%, 88.3%, and 97.6% for predicting exact size, ± one size, and ± two sizes, respectively. The elastic-net penalized linear regression (ENPLR) algorithm had the best performance for tibial component size (MAE 0.70, RMSE = 1.03) with accuracies of 43.8%, 90.0%, and 97.7% for predicting exact size, ± one size, and ± two sizes, respectively.

Conclusion

Machine learning algorithms demonstrated good-to-excellent accuracy for predicting within one size of the final tibial and femoral components used for TKA. Patient height and sex were the most important factors for predicting femoral and tibial component size, respectively. External validation of these algorithms is imperative prior to use in clinical settings.

Level of evidence

Case–control, III.

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Funding

No funding was received for this work.

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Authors and Affiliations

  1. Department of Orthopaedic Surgery, Hospital for Special Surgery, 535 E. 70th Street, New York, NY, USA

    Kyle N. Kunze

  2. School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA

    Evan M. Polce

  3. Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA

    Arpan Patel, Scott M. Sporer & Brett R. Levine

  4. Rothman Orthopaedic Institute at Thomas Jefferson University, Philadelphia, PA, USA

    P. Maxwell Courtney

Authors
  1. Kyle N. Kunze
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  2. Evan M. Polce
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  3. Arpan Patel
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  4. P. Maxwell Courtney
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  5. Scott M. Sporer
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  6. Brett R. Levine
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Contributions

KK: writing of initial manuscript, methodology, supervision. EP: writing of initial manuscript, data analysis. AP: data procurement. PMC, SS, BL: revision of initial manuscript.

Corresponding author

Correspondence to Kyle N. Kunze.

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The authors declare no conflict of interest in relation to the subject of work.

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This study received institutional board approval prior to commencement.

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Kunze, K.N., Polce, E.M., Patel, A. et al. Machine learning algorithms predict within one size of the final implant ultimately used in total knee arthroplasty with good-to-excellent accuracy. Knee Surg Sports Traumatol Arthrosc 30, 2565–2572 (2022). https://doi.org/10.1007/s00167-022-06866-y

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  • DOI: https://doi.org/10.1007/s00167-022-06866-y

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