ArticleBone & joint open2020
The application of machine learning to balance a total knee arthroplasty.
Article in Bone & joint open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence in total and unicompartmental knee arthroplasty.BMC musculoskeletal disorders · 2024Pooled 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
- Machine learning in knee arthroplasty: specific data are key-a systematic review.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2022Pooled it
- Accuracy assessment of an artificial intelligence model in predicting intraoperative bone resection parameters for robotic-assisted total knee arthroplasty.Scientific reports · 2026Article
- Intraoperative balance in total knee arthroplasty : Development and internal validation of the Knee Balancing Score.Orthopadie (Heidelberg, Germany) · 2026Article
- Artificial intelligence in total knee arthroplasty: clinical applications and implications.Knee surgery & related research · 2025Review
- Applications of machine learning and deep learning in musculoskeletal medicine: a narrative review.European journal of medical research · 2025Review
- Artificial intelligence in knee arthroplasty: current concept of the available clinical applications.Arthroplasty (London, England) · 2022Review
- A solid and strong beginning.Bone & joint open · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsThe use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments.
methodsBased on a clinical series of 479 primary total knees and 1,305 associated surgical decisions, various ML models were developed. These models identified the indicated surgical decision based on available, intra-operative alignment, and tibiofemoral load data.
resultsWith an associated area under the receiver-operator curve ranging between 0.75 and 0.98, the optimized ML models resulted in good to excellent predictions. The best performing model used a random forest approach while considering both alignment and intra-articular load readings.
conclusionThe presented model has the potential to make experience available to surgeons adopting new technology, bringing expert opinion in their operating theatre, but also provides insight in the surgical decision process. More specifically, these promising outcomes indicated the relevance of considering the overall limb alignment in the coronal and sagittal plane to identify the appropriate surgical decision.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.