Enhancing Knee Meniscus Damage Prediction from MRI Images with Machine Learning and Deep Learning Techniques

This paper investigates the application of machine learning and deep learning models to predict knee meniscus damage from magnetic resonance imaging (MRI) scans. We utilized the MRNet dataset, and processed it with different approaches, using a one-dimensional grayscale, RGB, and segmented images, complemented with features extracted using Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT) techniques. Our objective was to evaluate whether a DL model could match or exceed the diagnostic performance of clinical experts such as general radiologists and orthopedic surgeons. Our findings demonstrate that our ML and DL models can predict meniscal tears with comparable accuracy to that of general medical doctors. This suggests that ML and DL models have potential to deliver rapid preliminary results post-MRI exams and augment the quality of MRI diagnoses, particularly in settings lacking specialist radiologists. Thus, integrating ML and DL models into clinical practice could enhance the quality and consistency of MRI interpretation for knee meniscus damage.