Artificial intelligence can tell a person’s “real” age – simply by looking at their chest.
“The breakthrough marks a “leap” in medical imaging, and paves the way for improved early detection of several potentially deadly diseases,” said scientists.
Researchers at Osaka Metropolitan University in Japan developed an advanced AI model that utilizes chest radiographs to accurately estimate a patient’s chronological age.
When there is a disparity, it can signal a correlation with chronic disease.
The research team, led by graduate student Yasuhito Mitsuyama and Dr. Daiju Ueda, first constructed a deep learning-based AI model to estimate age from chest radiographs of healthy people.
They then applied the model to radiographs of patients with known diseases to analyze the relationship between AI-estimated age and each disease.
Given that AI trained on a single dataset is prone to overfitting, the researchers collected data from multiple institutions.
More than 67,000 chest radiographs were obtained between 2008 and 2021 from over 36,000 volunteers who underwent health check-ups at three facilities for the development, training, internal and external testing of the AI model for age estimation,
The developed model showed a correlation coefficient of 0.95 between the AI-estimated age and chronological age, according to the findings published in The Lancet Healthy Longevity.
“Generally, a correlation coefficient of 0.9 or higher is considered to be very strong,” said The Japanese team
To validate the usefulness of AI-estimated age using chest radiographs as a biomarker, an additional 34,197 chest radiographs were compiled from the same number of patients with known diseases from two other institutions.
The results revealed that the difference between AI-estimated age and the patient’s chronological age was “positively correlated” with several chronic diseases – including high blood pressure, chronic obstructive pulmonary disease (COPD) and hyperuricemia – elevated uric acid level in the blood
The researchers explained that, in other words, the higher the AI-estimated age compared to the chronological age, the more likely people were to have these diseases.
“Chronological age is one of the most critical factors in medicine,” said Mitsuyama.
“Our results suggest that chest radiography-based apparent age may accurately reflect health conditions beyond chronological age.”
He added: “We aim to further develop this research and apply it to estimate the severity of chronic diseases, to predict life expectancy, and to forecast possible surgical complications.”
Produced in association with SWNS Talker
Edited by Judy J. Rotich and Newsdesk Manager