ArticleKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2022
Machine learning algorithms predict within one size of the final implant ultimately used in total knee arthroplasty with good-to-excellent accuracy.
Article in Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The application of artificial intelligence in the design of highly compatible knee prostheses: a systematic review.BMC musculoskeletal disorders · 2026Pooled it
- 3D imaging-based AI models outperform demographic models and excel in tibial sizing compared with 2D models in total knee arthroplasty planning: A systematic review.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Pooled it
- Reliability of artificial intelligence in predicting total knee arthroplasty component sizes: a systematic review.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2024Pooled it
- Artificial Intelligence in Arthroplasty: A Comprehensive Structured Critical Review and Descriptive Evidence Map of Validation, Uncertainty, and Workflow Integration.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial intelligence applications in the management of musculoskeletal disorders of the shoulder: A systematic review.Journal of experimental orthopaedics · 2025Review
- Clinical validation of a deep learning-based approach for preoperative decision-making in implant size for total knee arthroplasty.Journal of orthopaedic surgery and research · 2024Article
- Leveraging transfer learning for predicting total knee arthroplasty failure from post-operative radiographs.Journal of experimental orthopaedics · 2024Article
- Development of an artificial intelligence model for predicting implant size in total knee arthroplasty using simple X-ray images.Journal of orthopaedic surgery and research · 2024Article
- Reliable prediction of implant size and axial alignment in AI-based 3D preoperative planning for total knee arthroplasty.Scientific reports · 2024Article
- The accuracy of preoperative implant size prediction achieved by digital templating in total knee arthroplasty is not affected by the quality of lateral knee radiographs.Journal of experimental orthopaedics · 2024Article
- Orthopaedic surgeons display a positive outlook towards artificial intelligence: A survey among members of the AGA Society for Arthroscopy and Joint Surgery.Journal of experimental orthopaedics · 2024Article
- The relationship between heavy metals and metabolic syndrome using machine learning.Frontiers in public health · 2024Article
- Application of Machine Learning Algorithms for Prognostic Assessment in Rotator Cuff Pathologies: A Clinical Data-Based Approach.Diagnostics (Basel, Switzerland) · 2023Article
- A role for artificial intelligence applications inside and outside of the operating theatre: a review of contemporary use associated with total knee arthroplasty.Arthroplasty (London, England) · 2023Review
- Imaging in Hip Arthroplasty Management-Part 1: Templating: Past, Present and Future.Journal of clinical medicine · 2022Review
- Artificial Intelligence Based Patient-Specific Preoperative Planning Algorithm for Total Knee Arthroplasty.Frontiers in robotics and AI · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop a novel machine learning algorithm capable of predicting TKA implant sizes using a large, multicenter database.
methodsA 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).
resultsA 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.
conclusionMachine 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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