AI algorithms can extract bone density data from routine CT scans to identify osteoporosis, enabling opportunistic screening.
Key Details
- 1NYU Langone Health and Visage developed an AI tool to analyze CT scans for bone loss indicators.
- 2Study used over 538,000 CT scans from 283,499 patients, covering multiple anatomical regions and CT machine models.
- 3Bone mineral density measurements accounted for age, gender, and race/ethnicity, revealing population trends.
- 4AI-based opportunistic screening could double osteoporosis testing rates and save over $2.5 billion annually in Medicare costs.
- 5Clinical trial is planned at NYU Langone Health for AI-based osteoporosis detection using existing CT images.
Why It Matters
This work demonstrates the practical value of applying AI to existing imaging data, potentially identifying millions of undiagnosed osteoporosis patients and reducing healthcare costs through earlier intervention. It exemplifies the expanding role of AI in repurposing radiology data for broader disease screening.
Source
EurekAlert
Related News
•EurekAlert
NYU AI Model Improves 5-Year Breast Cancer Risk Prediction via 3D Mammograms
NYU researchers developed an AI model using longitudinal 3D mammograms that outperforms single-scan and 2D-based tools in predicting 5-year breast cancer risk.
•EurekAlert
AI Framework Accelerates Aortic Aneurysm Risk Prediction from Imaging
Researchers developed BioPINN-LM, combining physics-informed neural networks and multimodal large language models to deliver fast, interpretable risk assessments for ascending thoracic aortic aneurysms.
•EurekAlert
Study Finds Patient Voices Missing in Generative AI Design for Oncology
A Flinders University-led review found patients and carers are rarely involved in shaping generative AI tools used in oncology.