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Published on: August 16, 2020
Using statistical modelling and machine learning in detecting bone properties: A systematic review protocol.
Osama Abdelhay1, Rand Alshoubaki1, Sana Murad1
1Department of Data Science and Artificial Intelligence, Princess Sumaya University for Technology, Amman, Jordan.
Plos One
|March 11, 2025
Summary
Artificial intelligence (AI) and machine learning (ML) show promise for improving osteoporosis detection. This systematic review will assess AI/ML tools for bone health, aiming for more accessible and accurate diagnostics.
Area of Science:
- Medical Imaging and Diagnostics
- Artificial Intelligence in Healthcare
- Bone Health Research
Background:
- Osteoporosis is a major health concern characterized by reduced bone mass and increased fracture risk.
- Traditional methods like Dual-energy X-ray Absorptiometry (DXA) have limitations in sensitivity and accessibility.
- Emerging AI and ML tools offer potential for enhanced analysis of complex medical data for osteoporosis detection.
Purpose of the Study:
- To systematically review and evaluate the application and effectiveness of AI and ML methods in detecting bone properties and osteoporosis.
- To compare the performance of AI/ML models against traditional diagnostic methods.
- To identify advancements and guide future research in AI/ML for bone health assessment.
Main Methods:
- Systematic review following PRISMA-P guidelines.
- Comprehensive literature search across major databases (PubMed, Embase, IEEE Xplore, Scopus, Cochrane Library, GitHub) until March 2025.
- Inclusion of studies on adults (40+ years) using AI/ML for bone density or properties; dual reviewer screening, data extraction, and risk of bias assessment.
- Data synthesis via narrative synthesis and meta-analysis using Review Manager and R software.
Main Results:
- This section will be populated upon completion of the systematic review and data analysis.
- Expected to identify and analyze various AI/ML models for osteoporosis detection.
- Will compare the effectiveness of AI/ML with traditional diagnostic methods.
Conclusions:
- AI and ML hold significant potential to revolutionize osteoporosis detection and prediction.
- Findings will inform healthcare professionals, researchers, and policymakers on AI/ML advancements in bone health.
- This review aims to facilitate the integration of AI/ML tools into routine osteoporosis screening and management.
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