Machine learning is changing osteoporosis detection: an integrative review

Area of Science:

• Artificial Intelligence in Medicine
• Medical Imaging Analysis
• Osteoporosis Research

Background:

• Early diagnosis and risk detection of osteoporosis are critical yet challenging medical issues.
• Traditional osteoporosis screening tools have limitations in accuracy and accessibility.
• Machine learning (ML) and artificial intelligence (AI) show significant promise for improving osteoporosis detection.

Purpose of the Study:

• To review the application of machine learning techniques for osteoporosis detection and screening.
• To analyze the evolution from basic ML algorithms to advanced deep learning (DL) methods.
• To discuss the challenges and future directions in ML-based osteoporosis diagnostics.

Main Methods:

• Review of recent research (past decade) on ML and DL in osteoporosis detection.
• Analysis of algorithms applied to clinical data, X-ray, CT, and MRI imaging.
• Comparison of basic ML algorithms with deep learning techniques for data processing and feature extraction.

Main Results:

• Basic ML algorithms are effective with structured clinical data but limited with high-dimensional imaging data.
• Deep learning algorithms demonstrate superior accuracy, particularly in image analysis and feature extraction.
• Challenges remain, including the 'black-box' nature of DL, data requirements, and clinical interpretability.

Conclusions:

• Machine learning, especially deep learning, offers improved accuracy and accessibility for osteoporosis detection.
• Future research should focus on model interpretability and integrating multimodal data for comprehensive skeletal health monitoring.
• The goal is to develop personalized, efficient, and accessible systems for early osteoporosis detection and prevention.