Injuries to the anterior cruciate ligament (ACL) are very common, and ACL injuries increase the risk of developing post-traumatic knee osteoarthritis and total knee replacement (TKR). At present, Magnetic Resonance Imaging (MRI) is the most effective imaging modality for distinguishing structural properties of the ACL in relation to adjacent musculoskeletal structures. Several multi-grading scoring systems have been developed to standardize reporting of knee joint abnormalities using MRI including the Whole-Organ Magnetic Resonance Imaging Scale (WORMS) and the Anterior Cruciate Ligament OsteoArthritis Score (ACLOAS). However, both of these grading metrics are susceptible to inter-rater variability.

Deep learning methods have recently shown potential to serve as an aid for clinicians with limited time or experience in osteoarthritis grading of the knee menisci and cartilage. Recently a team of scientists from the UCSF Center for Intelligent Imaging (ci2) evaluated the diagnostic utility of two convolutional neural networks (CNNs) for severity staging of anterior cruciate ligament (ACL) injuries. “Previous studies have developed binary classifiers to distinguish fully torn ACLs from intact ACLs,” said Nikan Namiri, medical student at UCSF School of Medicine and corresponding author. “And our study is the first to take deep learning a step further to help classify a broader spectrum of injury, which may be more useful in the clinical setting… Continue reading.