A deep learning model has identified and validated the first imaging biomarker for chronic stress using adrenal gland measurements from routine chest CT scans.
Key Details
- 1Researchers at Johns Hopkins developed an AI model to measure adrenal gland volume on chest CT scans.
- 2The study used data from 2,842 participants in the Multi-Ethnic Study of Atherosclerosis, integrating imaging, cortisol, and psychosocial stress measures.
- 3AI-derived Adrenal Volume Index (AVI) correlated with stress questionnaires, cortisol levels, and allostatic load.
- 4Higher AVI was associated with greater stress indicators and increased risk of heart failure and mortality over 10 years of follow-up.
- 5This method leverages routinely performed CT scans without additional testing or radiation.
Why It Matters
This is the first validated AI-driven imaging biomarker for chronic stress, offering radiologists a new quantitative tool for assessing the physiological impact of stress using existing CT data. The approach could improve risk stratification and guide preventive care for stress-related diseases.
Source
EurekAlert
Related News
•EurekAlert
UK Researchers Launch Unbiased AI Testing Platform for Diabetic Retinopathy Screening
Researchers unveiled a new independent platform for fair, large-scale evaluation of commercial AI algorithms detecting diabetic eye disease within the NHS.
•EurekAlert
Multi-Algorithm Machine Learning Model Enhances Exacerbation Risk Prediction in COPD
A multi-algorithm machine learning model significantly improves risk prediction of acute exacerbations in COPD patients using multidimensional clinical data.
•EurekAlert
AI Outperforms Radiologists in Pancreatic Cancer Detection on CT Scans
An international study finds that AI surpasses average radiologists in detecting pancreatic cancer on CT scans using a newly developed benchmark and dataset.