Newly devised AI model exhibits the capability to determine an individual’s age based on their chest X-ray

In a groundbreaking development, a newly created artificial intelligence (AI) model has showcased the ability to estimate a person’s age based on their chest X-ray, as reported in recent research published in The Lancet Healthy Longevity journal.

Furthermore, this AI model has exhibited its potential to detect chronic illnesses such as hypertension and chronic obstructive pulmonary disease (COPD) by discerning the difference between estimated age and chronological age.

Conducted by a team of researchers from Osaka Metropolitan University in Japan, this study marks a significant advancement in the field of medical imaging. It holds the potential to enhance early disease detection and facilitate timely interventions.

As the global population continues to age, research into aging and longevity becomes increasingly important. Aging, a complex process intricately linked with numerous diseases, impacts individuals in various ways.

The research team highlighted the crucial role of chronological age in medicine, underscoring its significance in assessing health status.

Lead researcher Yasuhito Mitsuyama told PTI, “Chronological age is one of the most critical factors in medicine. Our results suggest that chest radiography-based apparent age may accurately reflect health conditions beyond chronological age.”

To achieve age estimation, the AI model underwent rigorous training using a dataset comprising around 67,100 chest radiographs from 36,051 healthy individuals who had undergone health check-ups between 2008 and 2021.

The researchers uncovered a strong correlation between the age estimated by the AI model and the chronological age of the individuals.

The AI model’s capabilities were further refined as it explored the connection between AI-estimated age and various diseases.

For this purpose, an additional dataset containing 34,197 chest radiographs from patients with known diseases was utilized. Overall, the model was fine-tuned using a vast collection of approximately 101,300 chest X-rays gathered from 70,248 participants across five distinct institutions in Japan.

A significant finding emerged from the study: the gap between an individual’s AI-estimated age and their chronological age displayed a notable link with chronic conditions such as hypertension, hyperuricemia (characterized by elevated uric acid levels in the blood), and chronic obstructive pulmonary disease (COPD).

Consequently, the researchers concluded that individuals with higher AI-estimated ages had a greater likelihood of being affected by these aforementioned diseases.

The researchers have proposed that chest X-rays could serve as valuable biomarkers for assessing aging and longevity. Importantly, these images offer more than visual depictions of internal structures; they also provide insights into the complexities of internal organs and bones.