Gait-to-Contact (G2C): A Novel Deep Learning Framework to Predict Total Knee Replacement Wear from Gait Patterns.

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Medicine
• Orthopedic Surgery

Background:

• Total knee replacement (TKR) is a common surgery with wear rate variability influenced by gait patterns.
• Finite element analysis (FEA) models are accurate but computationally intensive, limiting research.
• Developing efficient methods to predict TKR wear is crucial for improving implant longevity.

Purpose of the Study:

• Introduce a novel deep learning (DL) surrogate model to predict polyethylene liner wear in TKR.
• Significantly reduce computational costs and processing time compared to traditional FEA.
• Enable faster and more accessible research into TKR wear mechanisms.

Main Methods:

• Generated 314 gait pattern variations (ISO14243-3:2014) for anterior/posterior translation, rotation, flexion/extension, and axial loading.
• Utilized a validated FEA model to compute linear wear distribution on polyethylene liners.
• Trained a transformer-CNN encoder-decoder DL model to predict wear distribution from gait time series data.

Main Results:

• The DL model achieved prediction times in minutes, drastically reducing the days required by FEA.
• Wear map predictions from the DL model showed high agreement with FEA results (MAPE < 6%, SSIM > 0.88, NMI > 0.88).
• The DL approach demonstrated substantial computational efficiency with comparable accuracy to FEA.

Conclusions:

• Deep learning offers a promising, computationally efficient alternative to FEA for predicting TKR wear.
• This methodology can accelerate research and potentially lead to personalized TKR interventions.
• Future work includes applying the DL model to clinical patient data for timely interventions.