Prediction of anterior cruciate ligament injury from MRI using deep learning

Knee injury emerges as one of the most common diseases, causing dislocation of knee joints, immobility, etc., in which the anterior cruciate ligament (ACL) injury is the most common one. The development of various artificial intelligence (AI) frameworks gained enormous attention in many areas, including injury prediction and health management via medical image analysis. The objective of the current study is to focus on a comprehensive high accurate prediction of ACL injury based on MRI medical images, and also demonstrate the ability of AI in practical and outline conceptual prediction and diagnosis frameworks for other types of knee injuries in the future. Our dataset comprised of knee MRI reports from Cho Ray Hospital, Vietnam which are composed of ACL and non-ACL injury patients. The MRI images were used as supporting data in the deep learning classification model with DenseNet-121 algorithm. The successful establishment of an ACL injury diagnosis model from MRI will pave the way for us to develop more diagnostic models of other injuries in the body as well as the prediction of bone diseases.