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A Prediction Model for Primary Anterior Cruciate Ligament Injury Using Artificial Intelligence.

Iskandar Tamimi1, Joaquin Ballesteros2, Almudena Perez Lara3

  • 1Knee Division, Hospital Regional Universitario de Málaga, Málaga, Spain.

Orthopaedic Journal of Sports Medicine
|September 27, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts anterior cruciate ligament (ACL) injuries using knee morphology. The AI model identified specific bone slope and meniscal height differences in patients with ACL tears, achieving over 90% accuracy.

Keywords:
ACLanterior cruciate ligamentanteroposterior lengthsartificial intelligencebone slopeinjurymachine learningmeniscal heightmeniscal slopepredictionrisk

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Area of Science:

  • Orthopaedic Surgery
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Supervised machine learning (AI) applications in orthopaedic surgery are limited.
  • AI models show potential for predicting various medical events.
  • Predicting anterior cruciate ligament (ACL) injuries remains a challenge.

Purpose of the Study:

  • To develop a predictive mathematical model for primary ACL injuries using AI.
  • To identify morphological knee features associated with ACL injuries.
  • To test the efficacy of supervised learning techniques in ACL injury prediction.

Main Methods:

  • A cross-sectional study involving 50 adult patients with primary ACL reconstruction and 50 matched controls.
  • Preoperative MRI scans were used to measure tibial plateau lengths, bone slopes (LBS, MBS), meniscal heights (LMH, MMH), and meniscal slopes (LMS, MMS).
  • A Gaussian Naïve Bayes model was implemented in Matlab R2019b to create the AI predictor.

Main Results:

  • Patients with ACL injuries exhibited significantly increased posterior lateral tibial bone slope (LBS) and lateral meniscal slope (LMS).
  • Lower medial and lateral meniscal heights (MMH, LMH) were observed in the ACL injury group.
  • The AI model, utilizing LBS and MBS, achieved 70% validation accuracy and 92% testing accuracy.

Conclusions:

  • A machine learning-based prediction model for primary ACL injury demonstrated high testing accuracy (>90%).
  • Specific morphological features, including posterior LBS, LMS, MMH, and LMH, are significantly different between ACL-injured patients and controls.
  • AI offers a promising tool for predicting ACL injuries based on knee morphology.