Machine learning is changing osteoporosis detection: an integrative review
Authors' conclusion
These issues highlighted the importance of model interpretability in future machine learning research.
Study context
- Population
- Research of the past decade on machine learning for osteoporosis detection and screening
- Comparison
- Basic machine learning algorithms with clinical data vs deep learning with imaging data (X-ray, CT, MRI) for osteoporosis detection
- Outcome
- Performance of machine learning approaches for osteoporosis detection
Main finding
As reported in the abstract
- Basic machine learning algorithms performed well with single structured data but had limitations with high-dimensional and unstructured imaging data
- Deep learning can significantly improve detection accuracy by automatically extracting image features, especially in image histological analysis
- Deep learning challenges: the "black-box" problem, heavy reliance on large amounts of labeled data, and difficulties in clinical interpretability
Original publication
Machine learning is changing osteoporosis detection: an integrative review. Osteoporos Int. Published 10 June 2025. doi:10.
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