One of the aims of successful total knee arthroplasty (TKA) is to restore the natural range of motion of the infected joint. The operated leg motion highly depends on the coordinate systems that have been used to prepare the bone surfaces for an implant. Assigning a perfect coordinate system to the knee joint is a considerable challenge. Various commercially available knee arthroplasty devices use different methods to assign the coordinate system at the distal femur. Transepicondylar axis (TEA) and Whiteside’s line are commonly used anatomical axes for defining a femoral coordinate system (FCS). However, choosing a perfect TEA for FCS is trickier, even for experienced surgeons, and a small error in marking Whiteside’s line leads to a misaligned knee joint. This work proposes a modified Whiteside’s line method for the selection of TEA. The Whiteside’s line, along with the knee center and femur head center, define two independent central planes. Multiple prominent points on the lateral and medial sides of epicondyles are marked. Based on the lengths of perpendicular distances between the multiple points and central planes, the most prominent epicondyle points are chosen to define an optimal TEA. Compared to conventional techniques, the modified Whiteside’s line defines a repeatable TEA

Total knee arthroplasty (TKA) is a surgical technique to replace damaged knee joints with artificial implants. Recently, the imageless TKA has brought a revolutionary improvement to the accuracy of implant placement and ease of surgical process. Based on key anatomical points on the knee, the software guides the surgeon during the TKA procedure. However, the number of revision surgeries is increasing due to malalignment caused by registration error, resulting in imbalanced contact stresses that lead to failure of the TKA. Conventional stress analysis methods involve time-consuming and computationally demanding finite element analysis (FEA). In this work, a machine-learning-based approach estimates the contact pressure on the TKA implants. The machine learning regression model has been trained using FEA data. The optimal preprocessing technique was confirmed by the data without preprocessing, data divided by model size, and data divided by model size and optimal angle. Extreme gradient boosting, random forest, and extra trees regression models were trained to determine the optimal approach. The proposed method estimates the contact stress instantly within 10 percent of the maximum error. This has resulted in a significant reduction in computational costs. The efficiency and reliability of the proposed work have been validated against the published literature.

This study presents a computational framework for the topology optimization of a patient-specific femoral component used in the total knee endoprosthesis. The motivation stems from the growing need to enhance implant longevity and biomechanical compatibility by optimizing internal structural design according to physiological loading conditions. A finite element–based density optimization method was employed to determine the optimal material distribution within the femoral component while maintaining anatomical geometry and functional constraints. The model was developed using realistic boundary conditions derived from knee joint mechanics, and the resulting design was compared with a conventional reference geometry. The optimized configuration exhibited more uniform stress distribution, reduced peak von Mises stresses, and improved mass efficiency without compromising mechanical stiffness. These findings demonstrate that the proposed method can significantly improve the structural performance and reliability of knee prostheses. The study concludes that integrating patient-specific modeling with topology optimization offers a promising pathway for developing advanced, individualized orthopedic implants and supports future experimental validation through 3D printing and biomechanical testing.