## 4 Discussion  
Computational analyses in biomechanics and medicine are valuable for surgery planning, medical device design, and understanding and preventing disease progression [174]. The ability to predict the initiation and progression of diseases would yield many benefits for patients, society, and the economies of countries. In this review, we focused on computational finite element analysis (FEA) aiming for an understanding of knee OA mechanisms and improvements for speeding up subject-specific models and simulations. Regarding planning surgical procedures, FEAs can be utilized to understand the effects of meniscectomies on cartilage stress distributions [175; 176]. [PERSON] et al. [176] simulated the effects of degenerative and radial tears of meniscus and meniscectomies on tissue stresses and displacements. Results indicated that performing a medial meniscectomy increases stresses in remaining tissue. Another application is design of surgical procedures for correcting extra- and intra-articular deformities associated with knee OA. [PERSON] et al. [177] proposed combining an opening-wedge high tibial osteotomy (OWHTO), related to extra-articular deformities, with a tibial condylar valgus osteotomy (TCVO), related to intra-articular deformities. FEAs and experimental tests on synthetic bone models revealed a comparable strength of the novel procedure to the currently used OWHTO procedure. In addition to surgical interventions, conservative options, such as gait retraining and weight loss, could also be aided and encouraged by computational models to prevent disease progression [178; 179; 22; 180]. [PERSON] et al. [181] showed the impact of a six-week gait retraining program on 16 subjects. They recommended specific gait retraining to reduce the knee adduction moment (KAM) in patients with medial knee OA. They used musculoskeletal modeling to estimate moments at the knee joint and assess the effect of the toe-in pattern trained during gait. For training, they used an instrumented treadmill with graphical interfaces to provide feedback to the patient during the trials. Results suggest the program effectively reduced the KAM in medial knee OA patients even six months after the training. The authors recommended complementing their study with additional FEA analyses. These variations in the KAM can easily be implemented in FEA models to estimate the consequence of these rehabilitation protocols on the knee joint tissues. As shown, FEA is currently utilized and recommended for complementing other approaches. Thus, for subject-specific analysis towards clinical applications in OA, we identified that certain phases in the current pipeline can be accelerated. In pre-processing, several studies have proposed solutions for rapid image segmentation to obtain the patient-specific geometry of the joint and procedures for automated meshing. However, no approach has been implemented to rapidly indicate subject-specific input loadings, nor biomechanical adaptations of the patient, e.g., gait pattern alterations caused by pain during OA worsening. In this regard, there are proposals for rapid loading estimation that can be later linked with those focused on rapidly obtaining the knee geometry and mesh. For instance, [PERSON] et al. [180] implemented a workflow for the real-time estimation of the knee medial tibiotemporal contact force. Their method used a physics-based electromyogram-driven neuromusculoskeletal model. [PERSON] et al. [117] compared different machine learning algorithms to predict the activation of muscles around the knee as well as the knee and hip contact forces. They found that a recurrent neural network (RNN) offered the best predictive capabilities. The RNN was able to mimic the outcomes of musculoskeletal modeling, which is a physical-based method. Regarding accelerating the processing stage, not much information was given in the literature within our scope.