Detection of Tibiofemoral Joint Injury in High-Impact Motion Based on Neural Network Reconstruction Algorithm

In order to reduce the damage degree of joint bones, ligaments, and soft tissues caused by the high impact on the tibiofemoral joint during landing, a method for detecting the damage of tibiofemoral joint under high-impact action based on neural network reconstruction algorithm is proposed. Two dimensional X-ray images of knee joints from straightening to bending in 10 healthy volunteers were selected. CT scans were performed on the knee joint on the same side, and the 3D model from the acquired images was reconstructed. The kinematics data of the femur relative to the tibia with full degree of freedom were measured by registering the 3D model with 2D images. The results showed that in the extended position, the femur was rotated inward (5.5° ± 6.3°) relative to the tibia. The range of femoral external rotation is (18.7° ± 5.9°) from flexion to 90° in straight position. However, from 90° to 120°, a small amount of internal rotation occurred (1.4° ± 1.9°), so during the whole flexion process, the femur rotated (17.3° ± 6.9°), among which, from the straight position to 15°, the femur rotated (10.0° ± 5.6°). Damage in different areas is determined by the size of the interlayer displacement sample size method of sample space reduction. It is proved that the detection method of tibiofemoral joint injury in high-impact motion based on neural network reconstruction algorithm has high accuracy and consistency.