Application of CT Medical Imaging Combined with Deep Learning 3D Reconstruction in the Diagnosis and Rehabilitation of Anterior Cruciate Ligament Injury in Table Tennis Players

Abstract

Because of the intense competition, table tennis requires players to bear a strong physiological load, which increases the risk of sports injury. Anterior cruciate ligament (ACL) is an important structure of the knee joint to maintain forward stability and rotational stability and is also a common sports injury in table tennis players. ACL has poor self-repair ability after injury. Therefore, the purpose of this study is to provide a more comprehensive, reliable, and representative theoretical basis for the diagnosis and rehabilitation of anterior cruciate ligament injury in table tennis players, and three-dimensional reconstruction of ACL using dual-source computed tomography (DSCT) combined with deep learning was conducted. For this purpose, a number of table tennis players with ACL injuries were collected, and each patient underwent arthroscopic anterior cruciate ligament reconstruction. DSCT scanning was performed on several knee joints, the 3D model of the knee joint was reconstructed using a CT image postprocessing workstation, and the medial wall of the femoral lateral condyle was reconstructed, as well as the reconstructed single tract of bony canal, tibial plateau, and bony canal. Then, the Lysholm score was used to score the cases, with scores greater than 75 as the excellent group and below 75 as the poor group. The relative positions of the central points of the femoral and tibial canals were marked and measured. The results were as follows: 3D-CT reconstruction could clearly reflect the situation after anterior cruciate ligament reconstruction. In clinic, it is used to evaluate the relationship between bone tunnel location and graft shape so as to guide the surgeon to improve the operation.