An Iterative Study of MRI, Clinical Evaluation, and Machine Learning Techniques for Knee based injury Diagnosis

DOI

10.2991/978-94-6463-718-2_13How to use a DOI?

Keywords

ACL Tear; MRI; Clinical Evaluation; Machine Learning; Diagnosis Methods

Abstract

Diagnosing ACL tears is challenging due to variable presentations and limited diagnostic tools. This study evaluates methods such as Magnetic Resonance Imaging (MRI), clinical evaluation, arthroscopy, and advanced machine learning (ML) models. MRI, the gold standard for ACL diagnosis, is noninvasive and highly sensitive, while clinical tests like the Lachman, pivot shift, and anterior drawer tests provide immediate diagnostic insights but depend on examiner expertise. Arthroscopy, though accurate, is invasive and not routinely used for diagnosis. Emerging ML techniques, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forest classifiers, show potential for enhancing diagnostic sensitivity by analyzing MRI images with high precision. These models reduce variability in interpretation, benefiting low-resource settings with limited expert radiologists. However, challenges such as the need for large annotated datasets, model generalizability across populations, and interpretability of predictions remain significant barriers. Additionally, user-friendly interfaces and clinical validation are essential for ML integration into practice. This review analyzes the performance metrics of various diagnostic tools using diverse datasets to assess their real-world applicability. Addressing gaps like dataset availability and standardized evaluation metrics can advance ACL tear diagnosis. Integrating modern ML techniques with traditional diagnostic methods may enhance accuracy, efficiency, and accessibility, reducing the burden on healthcare systems while benefiting clinicians and patients alike. This study aims to guide future research and improve clinical practices for ACL tear management.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Tushar Bhagwan Wagh
AU  - Mahip M. Bartere
AU  - Sonal Patil
PY  - 2025
DA  - 2025/05/23
TI  - An Iterative Study of MRI, Clinical Evaluation, and Machine Learning Techniques for Knee based injury Diagnosis
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 138
EP  - 145
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_13
DO  - 10.2991/978-94-6463-718-2_13
ID  - Wagh2025
ER  -