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Establishing correlation between bone mineral apposition rate and physiological loading using machine learning

Rakesh Kumar1, Siddhanth Das2, Davis Fernandes2

  • 1Department of Mechanical Engineering, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India.

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|November 25, 2025
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Summary
This summary is machine-generated.

This study explores how mechanical loading affects bone formation in post-menopausal osteoporosis. Machine learning models, particularly XGBoost Regressor, accurately predict bone mineral apposition rate (BMAR), identifying frequency as a key factor.

Keywords:
Bone adaptationMachine learning regressorMineral apposition rateStrain

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Area of Science:

  • Biomechanical Engineering
  • Orthopaedic Research
  • Computational Biology

Background:

  • Post-menopausal osteoporosis is a significant global health issue impacting bone density.
  • Decreased estrogen production with aging inhibits bone mineral apposition rate (BMAR).
  • Understanding the relationship between mechanical loading and BMAR is crucial for developing effective treatments.

Purpose of the Study:

  • To investigate the correlation between mechanical loading parameters (strain magnitude, frequency, cycles) and BMAR.
  • To evaluate the performance of various machine learning regressors in predicting BMAR.
  • To identify the most influential loading parameters affecting BMAR.

Main Methods:

  • Utilized experimental data to train and validate machine learning models.
  • Employed Random Forest Regressor, Support Vector Machine Regressor, K-Nearest Neighbors Regressor, and XGBoost Regressor.
  • Simulated feature importance of loading parameters using the validated models.

Main Results:

  • XGBoost Regressor demonstrated superior performance in predicting BMAR on both periosteal and endosteal surfaces.
  • Achieved high correlation coefficients (R²=0.945 on periosteal, R²=0.98 on endosteal) and low mean squared error (MSE).
  • Identified loading frequency as the most significant factor influencing BMAR on both bone surfaces.

Conclusions:

  • XGBoost Regressor offers the highest accuracy for BMAR prediction in this context.
  • The endosteal surface shows greater potential for accurate BMAR estimation compared to the periosteal surface.
  • Mechanical loading parameters, especially frequency, play a critical role in bone formation and can be modeled using machine learning.