ReviewArthroplasty (London, England)2023
Application of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty: a systematic review.
Review in Arthroplasty (London, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Machine Learning Based Diagnostic Models for Hip and Knee Prosthetic Joint Infection: A Systematic Review.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2026Pooled it
- Application of artificial intelligence in X-ray imaging analysis for knee arthroplasty: A systematic review.PloS one · 2025Pooled it
- Artificial Intelligence in Arthroplasty: A Comprehensive Structured Critical Review and Descriptive Evidence Map of Validation, Uncertainty, and Workflow Integration.Bioengineering (Basel, Switzerland) · 2026Review
- Periprosthetic Joint Infection: Biofilm Pathogenesis, Immune Dysregulation, and Emerging Prosthetic Interface Strategies.Biology · 2026Review
- Optimizing the management of periprosthetic infections with artificial intelligence: current evidence and future directions.EFORT open reviews · 2026Review
- Machine Learning-Driven Probability Scoring Enhances Diagnostic Certainty and Reduces Costs in Suspected Periprosthetic Joint Infection.Diagnostics (Basel, Switzerland) · 2026Article
- Development and Validation of a Predictive Model for Surgical Site Infection in Open Hand Injuries.Infection and drug resistance · 2026Article
- Review
- Quality assurance in machine Learning: Do we hit the mark for total joint arthroplasty models? A systematic review.Journal of clinical orthopaedics and trauma · 2025Review
- Beyond Cultures: The Evolving Role of Molecular Diagnostics, Synovial Biomarkers and Artificial Intelligence in the Diagnosis of Prosthetic Joint Infections.Journal of clinical medicine · 2025Review
- Periprosthetic joint infection after arthroplasty: advances and future prospects.Journal of orthopaedic surgery and research · 2025Review
- Predicting periprosthetic joint Infection: Evaluating supervised machine learning models for clinical application.Journal of orthopaedic translation · 2025Article
- Artificial Intelligence in Orthopedic Surgery: Current Applications, Challenges, and Future Directions.MedComm · 2025Review
- Article
- Editorial: Advances in artificial intelligence and robotics in joint arthroplasty.Arthroplasty (London, England) · 2025Article
- A practical guide to the implementation of AI in orthopaedic research-Part 7: Risks, limitations, safety and verification of medical AI systems.Journal of experimental orthopaedics · 2025Review
- Predicting periprosthetic joint infection in primary total knee arthroplasty: a machine learning model integrating preoperative and perioperative risk factors.BMC musculoskeletal disorders · 2025Article
- Preoperative Diagnosis of Periprosthetic Infection in Patients Undergoing Hip or Knee Revision Arthroplasties: Development and Validation of Machine Learning Algorithm.Diagnostics (Basel, Switzerland) · 2025Article
- Machine Learning Strategies for Preoperative PJI Diagnosis: Integrative Analysis of Serum and Synovial Fluid Markers.Journal of inflammation research · 2025Article
- Can Artificial Intelligence Reliably and Accurately Measure Lower Limb Alignment: A Systematic Review and Meta-Analysis.The archives of bone and joint surgery · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning is a promising and powerful technology with increasing use in orthopedics. Periprosthetic joint infection following total knee arthroplasty results in increased morbidity and mortality. This systematic review investigated the use of machine learning in preventing periprosthetic joint infection.
methodsA systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed was searched in November 2022. All studies that investigated the clinical applications of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty were included. Non-English studies, studies with no full text available, studies focusing on non-clinical applications of machine learning, reviews and meta-analyses were excluded. For each included study, its characteristics, machine learning applications, algorithms, statistical performances, strengths and limitations were summarized. Limitations of the current machine learning applications and the studies, including their 'black box' nature, overfitting, the requirement of a large dataset, the lack of external validation, and their retrospective nature were identified.
resultsEleven studies were included in the final analysis. Machine learning applications in the prevention of periprosthetic joint infection were divided into four categories: prediction, diagnosis, antibiotic application and prognosis.
conclusionMachine learning may be a favorable alternative to manual methods in the prevention of periprosthetic joint infection following total knee arthroplasty. It aids in preoperative health optimization, preoperative surgical planning, the early diagnosis of infection, the early application of suitable antibiotics, and the prediction of clinical outcomes. Future research is warranted to resolve the current limitations and bring machine learning into clinical settings.
Indexed as
Identifiers
What OpenQuestion holds
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.