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Knee osteoarthritis (KOA) is a degenerative joint disease predominantly affecting the elderly and is often managed through knee replacement surgeries. West Asians, who frequently engage in activities involving bending and kneeling during their prayers, tend to exhibit distinct bone anatomy compared to the Caucasian population. This research posits that patient-specific, customized knee implants with articular cartilages may lead to reduced post-surgical discomfort and a better implant fit compared to conventional standard implants. This study presents a novel concept and approach for the development of a customized total knee implant with articular cartilages, specifically tailored to simulate loading conditions using finite element analysis (FEA) for the Saudi Arabian population. The research analyses patient-specific customized knee implants with articular cartilages under both pre- and post-implant conditions using a finite element model (FEM). Computed tomography (CT) images of patients were utilized to create a solid model, which was then analysed under various constraints and conditions. The meshing process employed tetrahedral elements, converging with 76,197 nodes and 43,009 elements. The analysis was conducted under different body weights, specifically 75-80 kg and 100-105 kg. Two sets of force and moment were applied: the first with a force of 1000 N and a moment of 1.5 N-m, and the second with a force of 750 N and a moment of 0.8 N-m. The results indicated a 5% reduction in stress with implants designed for a 100 kg body weight, along with a significant reduction in ligament strain when compared to conventional knee joint stresses. This study offers a promising pathway toward reducing post-surgical discomfort. The proposed innovative solution has the potential to revolutionize total knee implant technology, offering enhanced functionality and improved patient outcomes for the Saudi Arabian population.

Scoliosis is a medical condition in which a person's spine has an abnormal curvature and Cobb angle is a measurement used to evaluate the severity of a spinal curvature. Presently, automatic Existing Cobb angle measurement techniques require huge dataset, time-consuming, and needs significant effort. So, it is important to develop an unsupervised method for the measurement of Cobb angle with good accuracy. In this work, an unsupervised local center of mass (LCM) technique is proposed to segment the spine region and further novel Cobb angle measurement method is proposed for accurate measurement. Validation of the proposed method was carried out on 2D X-ray images from the Saudi Arabian population. Segmentation results were compared with GMM-Based Hidden Markov Random Field (GMM-HMRF) segmentation method based on sensitivity, specificity, and dice score. Based on the findings, it can be observed that our proposed segmentation method provides an overall accuracy of 97.3% whereas GMM-HMRF has an accuracy of 89.19%. Also, the proposed method has a higher dice score of 0.54 compared to GMM-HMRF. To further evaluate the effectiveness of the approach in the Cobb angle measurement, the results were compared with Senior Scoliosis Surgeon at Multispecialty Hospital in Saudi Arabia. The findings indicated that the segmentation of the scoliotic spine was nearly flawless, and the Cobb angle measurements obtained through manual examination by the expert and the algorithm were nearly identical, with a discrepancy of only ± 3 degrees. Our proposed method can pave the way for accurate spinal segmentation and Cobb angle measurement among scoliosis patients by reducing observers' variability.