HQ Team
August 18, 2023: Japan’s radiologists using chest radiographs developed an AI model to accurately predict a human’s chronological age and detect chronic diseases.
Scientists at the Osaka Metropolitan University used artificial intelligence to arrive at the medical imaging model for early detection and intervention, according to a university statement.
Chronological age is the amount of time that has elapsed from birth to a given date and is the main way of defining age. Biological aging occurs as a person gradually accumulates damage to various cells, according to the US-based National Institutes of Health.
The researchers constructed a deep learning-based AI model to estimate age from chest radiographs of healthy individuals.
Prone to ‘overfitting’
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, according to the statement.
For the development, training, and internal and external testing of the AI model for age estimation, a total of 67,099 chest radiographs were obtained between 2008 and 202.
The radiographs were taken from 36,051 healthy individuals who underwent health check-ups at three facilities.
The developed model showed a correlation coefficient of 0.95 between the AI-estimated age and chronological age. Generally, a correlation coefficient of 0.9 or higher is considered to be very strong.
Hypertension, pulmonary disease
To validate the usefulness of AI-estimated age using chest radiographs as a biomarker, an additional 34,197 chest radiographs were compiled from 34,197 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 a variety of chronic diseases, such as hypertension, hyperuricemia, and chronic obstructive pulmonary disease.
The higher the AI-estimated age compared to the chronological age, the more likely individuals were to have these diseases.
The research team was led by graduate student Yasuhito Mitsuyama and Dr. Daiju Ueda from the Department of Diagnostic and Interventional Radiology at the Graduate School of Medicine.
“Chronological age is one of the most critical factors in medicine,” Mr Mitsuyama said.
Predict life expectancy
“Our results suggest that chest radiography-based apparent age may accurately reflect health conditions beyond chronological age. 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.”
Osaka Metropolitan University is the third largest public university in Japan, formed by a merger between Osaka City University and Osaka Prefecture University in 2022.
The results are set to be published in The Lancet Healthy Longevity.
