MRI Imaging Techniques and Artificial Ligaments for the Diagnosis and Reconstruction of Cruciate Ligament Injuries of the Knee Joint

Abstract

This study focused on the use of MRI imaging techniques and artificial ligaments in diagnosing and reconstructing Cruciate Ligament (CL) injuries of the knee joint. CL injuries are common in athletes and can cause significant pain and disability. MRI imaging is a powerful diagnostic tool that accurately identifies the extent and location of the injury. The use of Artificial ligaments has been increasing in the surgical reconstruction of the ligament. In this study, we offer a deep learning (DL)-a based method called ThreeFold CNN for arthroscopically detecting injuries to the CL in the knee during MRI. The CL on MRI was first separated using two deep convolutional neural networks (2DCNNs). Then structural problems within the isolated ligament were detected using a classification CNN to create a completely automated DL-based diagnostic method. We employ the Satin Bowerbird Optimizer (SBO) method for accelerating the performance of the model. The CL injury diagnosis system's specificity and sensitivity are 97 and 97, respectively, at the optimum threshold. In contrast, the clinical radiologists' specificity varied between 0.91 and 0.99, while their sensitivity was between 0.97 and 0.99. The diagnostic performance of the CL injury diagnosis system and CR did not differ in a statistically significant manner at$P<.05$. The CL injury diagnosis system's area under the ROC curve was 98.5, which indicates outstanding overall diagnostic accuracy.

Keywords:
Cruciate ligament (CL); MRI; artificial ligaments; injury diagnosis

HIGHLIGHTS

Structural problems within the isolated ligament were detected

Satin Bowerbird Optimizer (SBO) method for accelerating the performance of the model.

CL injury diagnosis system's area under the ROC curve was 98.5.