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Prediction of Temperature and Loading History Dependent Lumbar Spine Biomechanics Under Cyclic Loading Using

Nadja Blomeyer1, Saurabh Balkrishna Tandale2, Luis Fernando Nicolini2,3

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Summary

This study demonstrates how prolonged cyclic loading affects spinal biomechanics. A Recurrent Neural Network (RNN) accurately predicts changes in moment-range of motion curves, offering insights into lower back pain mechanisms.

Keywords:
BiomechanicsLoading-historyRecurrent Neural NetworkSpineViscoelasticity

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Area of Science:

  • Biomechanics
  • Spinal Mechanics
  • Computational Biology

Background:

  • Extended cyclic loading of the spine is linked to lower back pain (LBP).
  • Understanding loading history's impact on spinal structural behavior, including viscoelastic effects, is crucial for LBP research.
  • Previous studies highlight the correlation between spinal loading and pain, necessitating advanced predictive models.

Purpose of the Study:

  • To investigate the effects of extended cyclic loading on human spinal segments (L4-L5).
  • To develop and validate a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) for predicting spinal biomechanical responses.
  • To analyze the correlation between loading history, testing time, and changes in moment-range of motion (RoM) curves.

Main Methods:

  • Six human spinal segments (L4-L5) underwent cyclic pure moment loading (up to 7.5 Nm) for 18 hours per segment.
  • Loading protocols included flexion-extension (FE), axial rotation (AR), and lateral bending (LB), with rest periods.
  • An LSTM RNN was trained using loading history, testing time, and temperature to predict moment-RoM curves.

Main Results:

  • A significant positive correlation was found between total testing time and the ratio of the third to last loading cycle (p < 0.001 for BT, p < 0.05 for RT).
  • The RNN model achieved high accuracy (R²=0.988) in predicting moment-RoM curves.
  • The model successfully incorporated testing time and temperature as predictive inputs.

Conclusions:

  • Recurrent Neural Networks (RNNs) are feasible for predicting evolving moment-RoM curves under cyclic loading.
  • This predictive capability can enhance the understanding of spinal viscoelasticity and its role in lower back pain.
  • The study provides a novel computational approach to analyze long-term spinal loading effects.