Perrone Mattia, Simmons Scott, Malloy Philip, Karas Vasili, Yuh Catherine, Martin John, Mell Steven P
Rush University Medical Center, Chicago, IL, USA.
Drury University, Springfield, MO, USA.
Ann Biomed Eng. 2025 Sep 27. doi: 10.1007/s10439-025-03863-3.
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.
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).
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.
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.
全膝关节置换术（TKR）是美国最常见的住院手术。利用有限元分析（FEA）模型的研究表明，步态模式的可变性会导致TKR环境中磨损率的显著差异。然而，FEA模型执行起来可能资源密集且耗时，阻碍了该领域的进一步研究。本研究引入了一种新颖的基于深度学习的替代建模方法，旨在与传统FEA模型相比显著降低计算成本和处理时间。
采用一种已发表的方法生成ISO14243-3（2014）前/后平移、内/外旋转、屈伸和轴向加载时间序列的314种变化，并使用经过验证的FEA模型计算聚乙烯衬垫上的线性磨损分布。训练了一个具有基于Transformer-CNN的编码器-解码器架构的深度学习模型，以步态模式时间序列作为输入来预测线性磨损分布。通过使用磨损疤痕相关几何特征的平均绝对百分比误差（MAPE）、结构相似性指数测量（SSIM）和归一化互信息（NMI）等指标比较深度学习和FEA模型预测来评估模型性能。
与FEA相比，深度学习模型显著减少了生成磨损预测的计算时间，前者在几分钟内即可完成训练和推理，而后者则需要数天时间。深度学习模型磨损图预测与FEA结果的比较显示，大多数变量的MAPE值低于6%，SSIM和NMI值高于0.88，表明一致性程度较高。
深度学习方法为预测TKR中的磨损提供了一种有前景的替代FEA的方法，计算时间大幅减少且准确性相当。未来的研究旨在将这种方法应用于临床患者数据，这可能会在TKR环境中带来更个性化和及时的干预措施。
