约翰·霍普金斯研究人员发现了一个使用胸部CT扫描的AI模型,可以通过肾上腺大小检测慢性压力,为预测心脏风险提供非侵入性、准确的方法。 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.

约翰·霍普金斯大学的研究人员开发了一种AI模型,通过测量肾上腺体积(一种与长期压力相关的生物标志),通过常规胸部CT扫描确定慢性压力。 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. 肾上腺素量指数(AVI)源自现有成像数据,与压力问卷、皮质醇水平、过压负荷、心衰竭和死亡的较高风险相关。 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. 与单一的皮质醇测试不同,AVI反映的是累积生理压力。 Unlike single cortisol tests, AVI reflects cumulative physiological stress. 经过长达10年的验证,该生物标志独立地预测了心血管结果,并能够进行广泛、非侵入性筛查,而无需额外的辐射或测试,这是在临床护理方面实现客观压力评估的一个重大步骤。 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.