The Patent Landscape of AI-Based Imaging Diagnostics for Osteoporosis: A Scoping Review.
Authors
Affiliations (4)
Affiliations (4)
- ORBIT Lab, College of Medicine and Biological Information Engineering, Northeastern University, Liaoning, Shenyang 110016, China.
- Department of Radiology, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, China.
- Department of Orthopaedics, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, PRChina.
- ORBIT Lab, College of Medicine and Biological Information Engineering, Northeastern University, Liaoning, Shenyang 110016, China and The University of Sydney, Sydney Musculoskeletal Health and The Kolling Institute, Northern Clinical School, Faculty of Medicine and Health and the Northern Sydney Local Health District, Sydney, NSW 2065, Australia.
Abstract
Osteoporosis is a widespread skeletal disorder characterized by reduced bone density and increased fracture risk. Early detection is critical to preventing disability and healthcare burden. A systematic search was conducted in Google Patents for documents filed between June 2022 and June 2025 that applied artificial intelligence or machine learning to osteoporosis detection using X-ray imaging. Patents were screened in three stages by independent reviewers according to predefined inclusion criteria. Eligible patents were analyzed and categorized according to technical objectives and methodological features. 19 patents met the inclusion criteria. Most originated from Asian countries. Deep learning architectures, primarily convolutional and generative models, were central to innovations in bone density estimation, fracture risk prediction, and image preprocessing. Several patents introduced novel approaches such as cross-modal image fusion, anatomical feature transfer, and data augmentation. Persistent challenges included limited interpretability, variability across datasets, and insufficient clinical validation. The identified patents demonstrate growing sophistication in AI/ML-based X-ray analysis, reflecting convergence toward standardized workflows for image enhancement, feature extraction, and risk prediction. Nevertheless, important gaps remain, including limited interpretability, variability across imaging conditions, and insufficient clinical validation. The reliance on complex architectures and synthetic data also raises concerns regarding generalizability and real-world deployment. Addressing these challenges will be essential to ensure reliable, scalable translation of AI/ML-driven osteoporosis diagnostics into routine clinical practice. The recent patents demonstrate rapid progress in artificial intelligence-driven osteoporosis diagnostics using radiographic imaging. Advances in transparency, model robustness, and clinical validation are essential to enable safe and effective translation of these technologies into clinical practice.