Gait-to-Contact (G2C): A novel deep learning framework to predict total knee replacement wear from gait patterns

Background: Total knee replacement (TKR) is the most common inpatient surgery in the US. Studies leveraging finite element analysis (FEA) models have shown that variability of gait patterns can lead to significant variability of wear rates in TKR settings. However, FEA models can be resource-intensive and time-consuming to execute, hindering further research in this area. This study introduces a novel deep learning-based surrogate modeling approach aimed at significantly reducing computational costs and processing time compared to traditional FEA models. Methods: A published method was used to generate 314 variations of ISO14243-3(2014) anterior/posterior translation, internal/external rotation, flexion/extension, and axial loading time series, and a validated FEA model was used to calculate linear wear distribution on the polyethylene liner. A deep learning model featuring a transformer-CNN based encoder-decoder architecture was trained to predict linear wear distribution using gait pattern time series as input. Model performance was evaluated by comparing the deep learning and FEA model predictions using metrics such as mean absolute percentage error (MAPE) for relevant geometric features of the wear scar, structural similarity index measure (SSIM) and normalized mutual information (NMI). Results: The deep learning model significantly reduced the computational time for generating wear predictions compared to FEA, with the former training and inferring in minutes, and the latter requiring days. Comparisons of deep learning model wear map predictions to FEA results yielded MAPE values below 6% for most of the variables and SSIM and NMI values above 0.88, indicating a high level of agreement. Conclusion: The deep learning approach provides a promising alternative to FEA for predicting wear in TKR, with substantial reductions in computational time and comparable accuracy. Future research will aim to apply this methodology to clinical patient data, which could lead to more personalized and timely interventions in TKR settings.