ArticleKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2023
Leveraging large, real-world data through machine-learning to increase efficiency in robotic-assisted total knee arthroplasty.
Article in Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Machine learning for operative time prediction in robotic ventral hernia repair patients: Perspective on predictive model development and validation.Langenbeck's archives of surgery · 2026Article
- Time neutrality and reduced ecological burden - handheld robotic-assisted total knee arthroplasty improves operating room efficiency compared with manual total knee arthroplasty.International orthopaedics · 2026Article
- Optimizing arthroplasty outcomes: the impact of artificial intelligence and robotic assistance.Annals of medicine and surgery (2012) · 2026Review
- Increasing research on personalized alignment concepts, robotic-assisted surgery and artificial intelligence in total knee arthroplasty: A bibliometric analysis of the last 10 years inJournal of experimental orthopaedics · 2026Review
- Smart scheduling of arthroplasty surgery with machine learning and optimisation improves operating room utilisation.BMJ health & care informatics · 2026Article
- Comment on: Healthcare resource utilization for VELYSJournal of robotic surgery · 2025Article
- Robotic total hip and knee arthroplasty: economic impact and workflow efficiency.Journal of robotic surgery · 2025Review
- Healthcare resource utilization for VELYS™ robotic-assisted solution compared to manual surgery for total knee arthroplasty.Journal of robotic surgery · 2025Article
- Trends of robotic total joint arthroplasty utilization in the United States from 2010 to 2022: a nationwide assessment.Journal of robotic surgery · 2025Article
- AI may enable robots to make a clinical impact in total knee arthroplasty, where navigation has not!Journal of experimental orthopaedics · 2024Article
- Adverse events related to robotic-assisted knee arthroplasty: a cross-sectional study from the MAUDE database.Archives of orthopaedic and trauma surgery · 2024Article
- Machine learning models to predict surgical case duration compared to current industry standards: scoping review.BJS open · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeIncreased operative time can be due to patient, surgeon and surgical factors, and may be predicted by machine learning (ML) modeling to potentially improve staff utilization and operating room efficiency. The purposes of our study were to: (1) determine how demographic, surgeon, and surgical factors affected operative times, and (2) train a ML model to estimate operative time for robotic-assisted primary total knee arthroplasty (TKA).
methodsA retrospective study from 2007 to 2020 was conducted including 300,000 unilateral primary TKA cases. Demographic and surgical variables were evaluated using Wilcoxon/Kruskal-Wallis tests to determine significant factors of operative time as predictors in the ML models. For the ML analysis of robotic-assisted TKAs (> 18,000), two algorithms were used to learn the relationship between selected predictors and operative time. Predictive model performance was subsequently assessed on a test data set comparing predicted and actual operative time. Root mean square error (RMSE), R
resultsMales, BMI > 40 kg/m
conclusionsThis study demonstrated that greater surgeon case volume, cementless fixation, manual TKA, female, older and non-obese patients reduced operative time. ML prediction of operative time can be more accurate than historical averages, which may lead to optimized operating room utilization. LEVEL OF EVIDENCE: III.
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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.