SynthesisActa orthopaedica2021
Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review.
Synthesis in Acta orthopaedica, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses that pooled it.
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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
33 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Revolutionizing spinal interventions: a systematic review of artificial intelligence technology applications in contemporary surgery.BMC surgery · 2024Pooled it
- Artificial intelligence in total and unicompartmental knee arthroplasty.BMC musculoskeletal disorders · 2024Pooled it
- Availability and reporting quality of external validations of machine-learning prediction models with orthopedic surgical outcomes: a systematic review.Acta orthopaedica · 2021Pooled it
- Patients With Femoral Neck Fractures Are at Risk for Conversion to Arthroplasty After Internal Fixation: A Machine-learning Algorithm.Clinical orthopaedics and related research · 2022Trial
- Large Language Models for Ankle Fracture Classification and Management Prediction from Routine Clinical Documentation: A Single-Center Exploratory Study.Journal of imaging informatics in medicine · 2026Article
- A Machine Learning-Based Clinical Decision Support Tool for Intertrochanteric Hip Fracture Patients to Predict Postoperative Anemia Risk: A Retrospective Cohort Study.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial intelligence in surgical decision-making across the perioperative continuum: a scoping review.Frontiers in digital health · 2026Review
- Review
- Evaluation of changes in prediction modelling in biomedicine using systematic reviews.BMC medical research methodology · 2025Article
- Hospital-related healthcare expenditure of impending versus completed pathological femur fractures: a propensity score matched study of 265 patients.Acta orthopaedica · 2025Article
- Using deep learning to predict postoperative pain in reverse shoulder arthroplasty patients.JSES international · 2025Article
- Methodology and development of a machine learning probability calculator: Data heterogeneity limits ability to predict recurrence after arthroscopic Bankart repair.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Article
- Development of Machine Learning-based Algorithms to Predict the 2- and 5-year Risk of TKA After Tibial Plateau Fracture Treatment.Clinical orthopaedics and related research · 2025Article
- Development and validation of machine learning models to predict perioperative transfusion risk for hip fractures in the elderly.Annals of medicine · 2024Article
- Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.Clinical orthopaedics and related research · 2024Article
- Assessing the potential role of ChatGPT in spine surgery research.Journal of experimental orthopaedics · 2024Article
- Opportunistic CT for Prediction of Adverse Postoperative Events in Patients with Spinal Metastases.Diagnostics (Basel, Switzerland) · 2024Article
- Development and external validation of a machine learning model for prediction of survival in undifferentiated pleomorphic sarcoma.Musculoskeletal surgery · 2024Article
- An Overview of Machine Learning in Orthopedic Surgery: An Educational Paper.The Journal of arthroplasty · 2023Review
- Determining patients with spinal metastases suitable for surgical intervention: A cost-effective analysis.Cancer medicine · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and purpose - Advancements in software and hardware have enabled the rise of clinical prediction models based on machine learning (ML) in orthopedic surgery. Given their growing popularity and their likely implementation in clinical practice we evaluated which outcomes these new models have focused on and what methodologies are being employed.Material and methods - We performed a systematic search in PubMed, Embase, and Cochrane Library for studies published up to June 18, 2020. Studies reporting on non-ML prediction models or non-orthopedic outcomes were excluded. After screening 7,138 studies, 59 studies reporting on 77 prediction models were included. We extracted data regarding outcome, study design, and reported performance metrics.Results - Of the 77 identified ML prediction models the most commonly reported outcome domain was medical management (17/77). Spinal surgery was the most commonly involved orthopedic subspecialty (28/77). The most frequently employed algorithm was neural networks (42/77). Median size of datasets was 5,507 (IQR 635-26,364). The median area under the curve (AUC) was 0.80 (IQR 0.73-0.86). Calibration was reported for 26 of the models and 14 provided decision-curve analysis.Interpretation - ML prediction models have been developed for a wide variety of topics in orthopedics. Topics regarding medical management were the most commonly studied. Heterogeneity between studies is based on study size, algorithm, and time-point of outcome. Calibration and decision-curve analysis were generally poorly reported.
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