"Chronic stress that persists over an extended period of time can lead to a higher risk of developing physical and mental health issues. Having a method to quantify stress levels can be a useful diagnostic tool for clinicians. Researchers at Johns Hopkins University School of Medicine have developed a new, noninvasive, artificial intelligence (AI) deep learning digital biomarker for chronic stress, which was unveiled at the recent annual meeting of the Radiological Society of North America (RSNA)."
"The team evaluated AVI vis-à-vis psychosocial factors, cortisol, and the cumulative physiological effects, called allostatic load, which were calculated from blood pressure, heart rate, glucose, BMI, hemoglobin, albumin, WBC, and creatinine. "We used a deep learning model to automatically segment adrenals in a three-dimensional manner, and after that, we measured adrenal volumes and used that as biomarker for our subsequence analysis," said lead author and Johns Hopkins postdoctoral research fellow Elena Ghotbi, M.D., in an RSNA video report."
A deep learning model automatically segments adrenal glands in three dimensions and measures adrenal volume as a noninvasive digital biomarker for chronic stress. The biomarker is evaluated alongside validated psychosocial indicators such as perceived stress questionnaires and depression scores, plus cortisol measurements. Cumulative physiological burden is quantified as allostatic load calculated from blood pressure, heart rate, glucose, body mass index, hemoglobin, albumin, white blood cell count, and creatinine. Adrenal glands play a central role in the neuroendocrine stress response, and prolonged stress increases risk for cardiovascular events, arrhythmia, inflammation, anxiety, and irritability. Automated 3D adrenal volumetry provides a quantifiable tool to integrate biological and psychosocial measures of chronic stress.
Read at Psychology Today
Unable to calculate read time
Collection
[
|
...
]