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Johns Hopkins researchers found an AI model using chest CT scans can detect chronic stress via adrenal gland size, offering a non-invasive, accurate way to predict heart risks.
Researchers at Johns Hopkins University have developed an AI model that identifies chronic stress using routine chest CT scans by measuring adrenal gland volume, a biomarker linked to long-term stress.
The Adrenal Volume Index (AVI), derived from existing imaging data, correlates with stress questionnaires, cortisol levels, allostatic load, and higher risks of heart failure and mortality.
Unlike single cortisol tests, AVI reflects cumulative physiological stress.
Validated over up to 10 years, the biomarker independently predicts cardiovascular outcomes and could enable widespread, non-invasive screening without additional radiation or testing, offering a major step toward objective stress assessment in clinical care.
Los investigadores de Johns Hopkins descubrieron que un modelo de IA que utiliza tomografías computarizadas del pecho puede detectar el estrés crónico a través del tamaño de la glándula suprarrenal, ofreciendo una forma no invasiva y precisa de predecir los riesgos cardíacos.