ArticleKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2023
Risk factors for secondary meniscus tears can be accurately predicted through machine learning, creating a resource for patient education and intervention.
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, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Machine learning models predicting risk of revision or secondary knee injury after anterior cruciate ligament reconstruction demonstrate variable discriminatory and accuracy performance: a systematic review.BMC musculoskeletal disorders · 2025Pooled it
- Machine learning-based prediction of meniscal tears in ACL reconstruction using BMI, time to surgery, injury mechanism, and Tegner activity score: A temporally validated decision tool.Journal of experimental orthopaedics · 2026Article
- A practical guide to the implementation of artificial intelligence in orthopaedic research-Part 3: How orthopaedic research benefits from the implementation of artificial intelligence.Journal of experimental orthopaedics · 2025Review
- Long-Term Results for Meniscus Repair.Current reviews in musculoskeletal medicine · 2025Review
- MV2SwimNet: A lightweight transformer-based hybrid model for knee meniscus tears detection.PloS one · 2025Article
- Implementing Machine Learning Models for Prediction of Gender-Affirming Mastectomy Complications: Estimating Performance and Accuracy.Aesthetic surgery journal. Open forum · 2025Article
- Artificial intelligence in medical and biological research: promise and perils of ChatGPT and DeepSeek in advancing healthcare.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Random forest identifies predictors of discharge destination following total shoulder arthroplasty.JSES international · 2024Article
- Graft failure within 2 years of isolated anterior cruciate ligament reconstruction is associated with increased risk of secondary meniscus tears.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2023Article
- IL-6 induces periostin production in human ACL remnants: a possible mechanism causing post-traumatic osteoarthritis.Journal of orthopaedic surgery and research · 2023Article
- Supervised machine learning and associated algorithms: applications in orthopedic surgery.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2023Review
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
purposeThis study sought to develop and internally validate a machine learning model to identify risk factors and quantify overall risk of secondary meniscus injury in a longitudinal cohort after primary ACL reconstruction (ACLR).
methodsPatients with new ACL injury between 1990 and 2016 with minimum 2-year follow-up were identified. Records were extensively reviewed to extract demographic, treatment, and diagnosis of new meniscus injury following ACLR. Four candidate machine learning algorithms were evaluated to predict secondary meniscus tears. Performance was assessed through discrimination using area under the receiver operating characteristics curve (AUROC), calibration, and decision curve analysis; interpretability was enhanced utilizing global variable importance plots and partial dependence curves.
resultsA total of 1187 patients underwent ACLR; 139 (11.7%) experienced a secondary meniscus tear at a mean time of 65 months post-op. The best performing model for predicting secondary meniscus tear was the random forest (AUROC = 0.790, 95% CI: 0.785-0.795; calibration intercept = 0.006, 95% CI: 0.005-0.007, calibration slope = 0.961 95% CI: 0.956-0.965, Brier's score = 0.10 95% CI: 0.09-0.12), and all four machine learning algorithms outperformed traditional logistic regression. The following risk factors were identified: shorter time to return to sport (RTS), lower VAS at injury, increased time from injury to surgery, older age at injury, and proximal ACL tear.
conclusionMachine learning models outperformed traditional prediction models and identified multiple risk factors for secondary meniscus tears after ACLR. Following careful external validation, these models can be deployed to provide real-time quantifiable risk for counseling and timely intervention to help guide patient expectations and possibly improve clinical outcomes. LEVEL OF EVIDENCE: III.
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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.