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Related Experiment Video

Updated: Sep 26, 2025

Anterior Cruciate Ligament Transection and Synovial Fluid Lavage in a Rodent Model to Study Joint Inflammation and Posttraumatic Osteoarthritis

Anterior Cruciate Ligament Transection and Synovial Fluid Lavage in a Rodent Model to Study Joint Inflammation and Posttraumatic Osteoarthritis

Published on: September 2, 2025

Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears.

Mazhar Javed Awan^1,2, Mohd Shafry Mohd Rahim¹, Naomie Salim¹

¹School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor 81310, Malaysia.

Journal of Healthcare Engineering

|April 21, 2022

Summary

Machine learning models accurately detect anterior cruciate ligament (ACL) tears by analyzing knee MRI data. This approach effectively handles imbalanced datasets, improving diagnostic accuracy for knee injuries.

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Area of Science:

Orthopedics
Radiology
Data Science

Background:

Knee osteoarthritis and anterior cruciate ligament (ACL) tears are significant causes of disability.
Early diagnosis of ACL tears is crucial to prevent progression and potentially avoid surgery.

Purpose of the Study:

To comparatively analyze machine learning models for diagnosing three types of ACL tears.
To address the challenge of imbalanced data distributions in ACL tear classification.

Main Methods:

Four machine learning models (Random Forest, Cat Boost, LGBM, ETC) were applied to a balanced ACL dataset.
Oversampling and hyperparameter tuning were used to improve model performance on imbalanced data.

Main Results:

The models achieved high accuracy (up to 98.26%) and an Area Under Curve (AUC) of approximately 0.998 after data balancing.
Models using imbalanced data performed poorly, with accuracy below 76%.

Conclusions:

Machine learning, particularly with balanced datasets, offers a highly accurate method for diagnosing ACL tears from MRI.
The developed models can potentially automate and improve the efficiency of ACL tear diagnosis, reducing reliance on radiologists.