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Updated: Jul 10, 2025

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model

Published on: September 28, 2017

A Machine Learning-Based Preclinical Osteoporosis Screening Tool (POST): Model Development and Validation Study.

Qingling Yang¹, Huilin Cheng¹, Jing Qin¹

¹School of Nursing, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China.

|November 21, 2023

Summary

A new machine learning tool, the Preclinical Osteoporosis Screening Tool (POST), accurately identifies individuals at high risk for osteoporosis. This accessible screening method aids in preventing fractures and guides clinical decisions.

Keywords:

Hong Kong health care machine learning older people osteoporosis screening tool

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Published on: September 8, 2023

Area of Science:

Gerontology
Medical Informatics
Public Health

Background:

Osteoporosis screening is a priority for fracture prevention.
Existing tools often lack specificity, leading to potential over-diagnosis or missed cases.
Developing precise, accessible screening methods is crucial for effective healthcare.

Purpose of the Study:

To create a high-performance, easily accessible preclinical risk screening tool for osteoporosis.
To utilize a machine learning-based approach tailored for the Hong Kong Chinese population.
To improve early detection and intervention for osteoporosis.

Main Methods:

Machine learning models (gradient boosting, SVM, naive Bayes, logistic regression) were developed and compared.
A validated questionnaire collected risk factors from 800 participants (aged ≥45).
Bone mineral density (BMD) measured osteoporosis; the best model formed the Preclinical Osteoporosis Screening Tool (POST).

Main Results:

The POST model identified 7 key predictors: age, gender, education, height loss, BMI, teeth loss, and vitamin D intake.
The POST achieved an Area Under the Curve (AUC) of 0.86.
POST demonstrated high sensitivity (0.83), specificity (0.83), and negative predictive value (0.98).

Conclusions:

The machine learning-based POST is a convenient and accurate tool for osteoporosis prediction.
POST shows potential for guiding population-based preclinical screening.
The tool can assist in clinical decision-making for osteoporosis management.