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Related Experiment Video

Updated: Nov 11, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Machine Learning Solutions for Osteoporosis-A Review.

Julien Smets¹, Enisa Shevroja¹, Thomas Hügle²

¹Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland.

Journal of Bone and Mineral Research : the Official Journal of the American Society for Bone and Mineral Research

|March 22, 2021

Summary

This summary is machine-generated.

Machine learning (ML) shows promise for osteoporosis and fracture detection, but current studies often lack quality. Improving reporting and validation is key for applying artificial intelligence (AI) in bone health management.

Keywords:

ARTIFICIAL INTELLIGENCE FRACTURE PREDICTION MACHINE LEARNING OSTEOPOROSIS RISK ASSESSMENT

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Author Spotlight: An Economic and Efficient Method for Quantitative Evaluation of Bone Microarchitecture in a Murine Osteoporosis Model

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Published on: September 8, 2023

Area of Science:

Biomedical Engineering
Data Science
Orthopedics

Background:

Osteoporosis and fractures are complex, multifactorial diseases requiring advanced analytical methods.
Machine learning (ML) and artificial intelligence (AI) offer potential for analyzing high-dimensional data in bone health.
Despite advancements, technical and clinical concerns exist regarding ML application in osteoporosis.

Purpose of the Study:

To review the application of ML in osteoporosis management.
To outline concerns and inform stakeholders on using AI for osteoporosis.
To identify promising ML applications and areas for improvement in research.

Main Methods:

Systematic literature search of PubMed and Web of Science.
Inclusion of 89 studies covering bone properties, classification, fracture detection, and risk prediction.
Quality assessment using a 12-point checklist to evaluate reporting and methodology.

Main Results:

Studies were of moderate quality (mode score 6, range 2-11).
Common limitations included incomplete reporting, inadequate data splitting, and lack of external validation.
Image-based opportunistic diagnosis and fracture detection showed promise.

Conclusions:

ML offers significant potential for osteoporosis diagnosis, fracture detection, and risk prediction.
Standardized checklists are recommended to improve ML model development and reporting.
Further research should focus on external validation and identifying novel risk factors.