Scientists from Osaka Metropolitan University have developed a groundbreaking AI model that can accurately estimate a person’s age by analyzing their chest radiographs. This innovative approach to age assessment has the potential to revolutionize disease detection and intervention, providing valuable insights into an individual’s overall health.
Traditionally, age assessment has been primarily based on facial appearance. However, this new AI-powered model shifts the focus to the chest, utilizing chest X-rays to develop biomarkers for aging. By analyzing the chest radiograph, the AI model can determine an individual’s chronological age with remarkable accuracy.
The key breakthrough lies in the correlation between age estimation and chronic diseases. When there is a discrepancy between the AI-estimated age and a person’s chronological age, it may indicate the presence of underlying chronic conditions. This correlation opens up new possibilities for early disease identification and intervention. By detecting these discrepancies, healthcare professionals can potentially intervene earlier, leading to better treatment outcomes and improved patient care.
The research team, led by graduate student Yasuhito Mitsuyama and Dr. Daiju Ueda, constructed a deep learning-based AI model using chest radiographs of healthy individuals. They then applied the model to radiographs of patients with known diseases to analyze the relationship between AI-estimated age and each disease. The study utilized data from multiple institutions to ensure the model’s accuracy and effectiveness.
To validate the usefulness of AI-estimated age as a biomarker, additional chest radiographs were compiled from patients with known chronic diseases. The results revealed a positive correlation between the difference in AI-estimated age and the presence of chronic diseases such as hypertension, hyperuricemia, and chronic obstructive pulmonary disease. This suggests that the higher the AI-estimated age compared to the chronological age, the greater the likelihood of these diseases.
This research has far-reaching implications for the field of medicine. It not only provides a novel and accurate method for age assessment but also offers insights into an individual’s health status beyond their chronological age. In the future, this AI model could be further developed to estimate the severity of chronic diseases, predict life expectancy, and anticipate possible surgical complications.
1. How does the AI model estimate age?
The AI model analyzes chest radiographs and utilizes deep learning techniques to estimate a person’s chronological age based on specific biomarkers associated with aging.
2. What is the significance of the correlation between AI-estimated age and chronic diseases?
The correlation suggests that deviations in AI-estimated age from the chronological age may indicate the presence of underlying chronic conditions. It provides healthcare professionals with early indicators for disease detection and intervention.
3. Can this AI model be used for other purposes?
Yes, in addition to age assessment, further research aims to utilize this AI model to estimate the severity of chronic diseases, predict life expectancy, and foresee possible surgical complications.
(Source: Osaka Metropolitan University)