A framework for designing, analyzing and classifying cementless femoral stem for malay population

Abstract

Asian hip morphology differs from western populations due to their lifestyle and physical stature. This was confirmed by the modification of commercial hip implants to address these differences and to improve the primary fixation stability inside the femoral canal. This study provided a framework for designing, analyzing and classifying cementless femoral stem for Malay population. The process began with a three dimensional (3D) morphology study, followed by a femoral stem design, fit and fill analysis, and nonlinear finite element analysis (FEA). Various femur parameters for periosteal and endosteal canal diameters were measured from the osteotomy level to 150 mm below, to determine the isthmus position. The 3D morphology study provided accurate dimensions that ensured primary fixation stability for the stem – bone interface and prevented stress shielding at the calcar region. The results showed better total fit (53.7%) and fill (76.7%) in the canal for this newly designed metaphyseal loading with mediolateral flared femoral stem. The FEA showed the maximum equivalent von Misses stress was 66.88 MPa proximally with a safety factor of 2.39 against endosteal fracture, and micromotion was 4.73 µm, which promotes osseointegration. The prototype was fabricated using 316L stainless steel by using investment casting techniques to reduce manufacturing cost without jeopardizing implant quality. Most researchers validated FEA with biomechanical testing but this increases computational time with different preset parameters. Any changes to these parameters will lead to different results, which are not in compliance with the experimental results. A new method for primary stability classification using support vector machine classifier and several time domain features for feature extraction (TDF – SVM) was proposed to overcome this FEA limitation. Thirteen different time domain features feed the classifier with polynomial kernel that mapped the datasets into separable hyper planes. Multiclass support vector machines considered three classes of micromotion and four classes of strain by mapping the original data into a feature space. A one-against-all method was chosen because of its easy application, reduced computational time, and accurate results. The results demonstrated more than 97% classification accuracy using several time domain features (mean absolute value, maximum peak value, mean value, root mean square) for both strain and micromotion. This indicated that TDF – SVM could be applied as preclinical tool to provide functional information for implant stability prior clinical use.