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Aging and its effect on bone remodeling is the most common cause of bone disorders. In young and healthy people, bone deposition and resorption happen at an equal rate to maintain optimal bone health.
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Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population
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Osteoporosis Prediction Using Machine-Learned Optical Bone Densitometry Data.

Kaname Miura1,2, Shigeo M Tanaka3, Chanisa Chotipanich4

  • 1Biological Engineering Program, King Mongkut's University of Technology Thonburi, Bangkok, 10140, Thailand.

Annals of Biomedical Engineering
|October 26, 2023
PubMed
Summary

Optical bone densitometry (OBD) combined with machine learning (ML) shows promise for osteoporosis screening. This approach accurately predicts bone density scores, aiding in early detection and diagnosis.

Keywords:
Demographic dataMachine learningOpticsOsteoporosis screening

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Data Science

Background:

  • Osteoporosis diagnosis relies on accurate bone density assessment.
  • Optical bone densitometry (OBD) offers a non-invasive method for bone health evaluation.
  • Machine learning (ML) techniques enhance diagnostic accuracy in medical screening.

Purpose of the Study:

  • To assess the feasibility of integrating OBD with ML for osteoporosis screening.
  • To develop and validate an ML model for predicting osteoporosis using OBD data.

Main Methods:

  • Collected Dual energy X-ray absorptiometry (DXA) and OBD measurements from 203 Thai participants.
  • Utilized ML techniques, specifically Ridge regressor, to predict DXA T-scores from OBD and demographic data.

Main Results:

  • The ML model achieved a correlation coefficient (r) of 0.512 for predicted T-scores against DXA reference values.
  • The model demonstrated an Area Under the Curve (AUC) of 0.853 in discriminating individuals with osteoporosis.

Conclusions:

  • The combined OBD and ML approach is a reliable tool for osteoporosis screening.
  • This method shows potential for improving early detection and diagnosis of osteoporosis.