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
AI Model Accurately Predicts Blood Loss Risk in Liposuction
A machine learning model predicts blood loss during high-volume liposuction with 94% accuracy.

•EurekAlert
AI-Driven CT Tool Predicts Cancer Spread in Oropharyngeal Tumors
Researchers have created an AI tool that uses CT imaging to predict the spread risk of oropharyngeal cancer, offering improved treatment stratification.

•EurekAlert
AI Model PRTS Predicts Spatial Transcriptomics From H&E Histology Images
Researchers developed PRTS, a deep learning model that infers single-cell spatial transcriptomics from standard H&E-stained tissue images.