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Updated: Jul 9, 2026

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Preoperative MRI and clinical indicators for predicting meniscal repairability: a machine learning-based study.

Peipei Hao1, Kun Cheng1, Yun Xu1

  • 1Department of Radiology, School of Medicine, Tongji Hospital, Tongji University, 389 Xincun Road, Putuo District, Shanghai, China.

Journal of Orthopaedic Surgery and Research
|July 4, 2026
PubMed
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A machine learning model using preoperative MRI scans can predict if a meniscal tear is repairable. This tool aids surgeons in making better decisions for arthroscopic knee surgery.

Area of Science:

  • Orthopedic Surgery
  • Radiology
  • Machine Learning

Background:

  • Meniscal tears are common knee injuries, and determining repairability preoperatively is crucial for surgical planning.
  • Current methods for assessing meniscal repairability often lack precision, leading to suboptimal surgical outcomes.

Purpose of the Study:

  • To develop and validate a machine learning (ML) model using preoperative MRI and clinical data to predict the repairability of meniscal tears.
  • To identify key imaging and clinical features that predict meniscal repairability.

Main Methods:

  • A retrospective analysis of 491 patients who underwent knee MRI and arthroscopy.
  • Features extracted from MRI included meniscal morphology, cartilage status, and tear characteristics. Clinical data and demographic variables were also included.
Keywords:
MRIMachine learningMeniscal repairability

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  • Multiple ML models (Logistic Regression, Random Forest, GBM, SVM) were trained and evaluated using cross-validation, with performance assessed by AUC, calibration, and DCA.

    • Main Results:

    • The logistic regression model achieved the highest predictive performance (AUC = 0.777).
    • Key predictors of non-repairability included ACL injury, high-grade cartilage degeneration, higher BMI, male sex, and greater tear displacement.
    • The model demonstrated good calibration and clinical utility across various decision thresholds.

    Conclusions:

    • A radiology-centered ML model integrating preoperative MRI features can accurately predict meniscal repairability.
    • This model shows potential to assist surgeons in optimizing arthroscopic decision-making and surgical planning.
    • Further prospective external validation is recommended before widespread clinical adoption.