Key points from article :
Scientists have developed an advanced AI model that uses chest radiographs to accurately estimate a patient's chronological age.
First, a deep learning-based AI model was constructed to estimate age from chest radiographs of healthy individuals.
67,099 chest radiographs were obtained from 36,051 healthy individuals for the development, training, and testing of AI model.
To validate the AI-estimated age, an additional 34,197 chest radiographs were compiled from 34,197 patients with known diseases.
Showed a very strong correlation coefficient of 0.95 between the AI-estimated age and chronological age.
When there is a disparity, it can signal a correlation with chronic disease.
Higher the AI-estimated age compared to the chronological age, the more likely individuals were to have diseases.
Further research aiming to estimate the severity of chronic diseases, predict life expectancy, and forecast possible surgical complications.
Findings mark a leap in medical imaging, paving the way for improved early disease detection and intervention.
Study led by Daiju Ueda from Osaka Metropolitan University, published in The Lancet Healthy Longevity.
