Accuracy of machine learning techniques for real-time prediction of implanted lower limb mechanics with comprehensive and reduced input parameters

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Evaluation shows biLSTM model significantly improves prediction accuracy in lower limb biomechanics, indicating efficiency in implant design.

The biLSTM model achieved approximately 45% lower normalized root mean square error (nRMSE) compared to other models.
A dataset from 1500 simulations was used to assess different surgical alignments and loading conditions.
The study identifies the minimum input dataset necessary for accurate biomechanical predictions.
Results suggest that using machine learning techniques can optimize implant positioning during surgery.

Maag et al. (2025) studied this question.

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Evaluation shows biLSTM model significantly improves prediction accuracy in lower limb biomechanics, indicating efficiency in implant design.