Using a deep learning AI model, researchers identified the first-of-its-kind biomarker of chronic stress detectable through routine imaging, according to research being presented next week at the annual meeting of the Radiological Society of North America (RSNA).
Chronic stress can affect both physical and psychological well-being, causing a variety of problems including anxiety, insomnia, muscle pain, high blood pressure and a weakened immune system, according to the American Psychological Association. Research shows that chronic stress can contribute to the development of major illnesses, such as heart disease, depression and obesity.
The study’s lead author, Elena Ghotbi, M.D., a postdoctoral research fellow at Johns Hopkins University School of Medicine in Baltimore, Maryland, developed and trained a deep learning model to measure adrenal gland volume on existing CT scans.
Each year, tens of millions of chest CT scans are performed in the United States alone.
“Our approach leverages widely available imaging data and opens the door to large-scale evaluations of the biological impact of chronic stress across a range of conditions using existing chest CT scans,” Dr. Ghotbi said. “This AI-driven biomarker has the potential to enhance cardiovascular risk stratification and guide preventive care without additional testing or radiation.”
Senior author Shadpour Demehri, M.D., professor of radiology at Johns Hopkins, said chronic stress is a prevalent condition or complaint that many adults deal with on a daily basis.
“For the first time, we can ‘see’ the long-term burden of stress inside the body, using a scan that patients already get every day in hospitals across the country. Until now, we haven’t had a way to measure and quantify the cumulative effects of chronic stress, other than questionnaires, surrogate serum markers like chronic inflammation, and cortisol measurement, which is very cumbersome to obtain.” Dr. Demehri said.
Each year, tens of millions of chest CT scans are performed in the United States alone.
“Our approach leverages widely available imaging data and opens the door to large-scale evaluations of the biological impact of chronic stress across a range of conditions using existing chest CT scans,” Dr. Ghotbi said. “This AI-driven biomarker has the potential to enhance cardiovascular risk stratification and guide preventive care without additional testing or radiation.”
Senior author Shadpour Demehri, M.D., professor of radiology at Johns Hopkins, said chronic stress is a prevalent condition or complaint that many adults deal with on a daily basis.
“For the first time, we can ‘see’ the long-term burden of stress inside the body, using a scan that patients already get every day in hospitals across the country. Until now, we haven’t had a way to measure and quantify the cumulative effects of chronic stress, other than questionnaires, surrogate serum markers like chronic inflammation, and cortisol measurement, which is very cumbersome to obtain.” Dr. Demehri said.