Medical Imaging Diagnosis of Anterior Cruciate Ligament Injury Based on Intelligent Finite-Element Algorithm

Abstract

A medical imaging method based on an intelligent finite-element algorithm was proposed to diagnose anterior cruciate ligament injury modeling better. CT three-dimensional finite-element modeling was used to predict the fixation points of the anterior cruciate ligament (ACL) femoral tunnel. In this study, 19 subjects were selected, including 11 males and 8 females. There were seven cases of the left knee and 12 cases of the right knee; all patients had sports injuries. The anatomical structure of a patient’s knee was transformed into a three-dimensional model using finite-element analysis software for segmentation. The models of the tibial plateau and lateral femoral condyle were retained. The results showed that the Lysholm score difference (D) between 6 months after surgery and 1 day before surgery was used as the dependent variable in the three-dimensional finite-element model of knee joint established by the software. Pearson’s correlation analysis was performed, and the difference $\left(P<0.05\right)$ was statistically significant. The original image of the Dicom format obtained through CT scan is preprocessed in Mimics without any format conversion, which avoids the loss of information, saves more time, and reduces the workload. The definition of “threshold” is used to complete the extraction of bone contour and realize automation. The speed and accuracy of modeling are improved.