New AI model can accurately estimate age from chest X-rays, research says

In a groundbreaking development, a newly devised artificial intelligence (AI) model has exhibited the capability to determine an individual's age based on their chest X-ray, according to recent research published in The Lancet Healthy Longevity journal.

Additionally, this AI model has demonstrated its potential to identify chronic illnesses such as hypertension and chronic obstructive pulmonary disease by discerning the disparity between estimated age and chronological age.

Conducted by researchers from Osaka Metropolitan University in Japan, this study signifies a significant stride forward in the realm of medical imaging. It holds the promise of enhancing early disease detection and facilitating timely interventions.

Significance of age in medical science

As our global population continues to age, research into ageing and longevity assumes increasing significance. Ageing, a multifaceted process intricately linked with numerous diseases, affects individuals in diverse ways.

The research team underscored the pivotal role of chronological age in the realm of medicine, emphasising its relevance 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."

AI's age estimation prowess

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

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

The AI model's capabilities were further honed as it delved into the relationship between AI-estimated age and various diseases.

An additional dataset comprising 34,197 chest radiographs from patients with known diseases was employed for this purpose. In total, the model was fine-tuned using a vast pool of around 101,300 chest X-rays, sourced from 70,248 participants across five different institutions in Japan.

AI's role as a disease indicator

A striking revelation emerged from the research: the disparity between an individual's AI-estimated age and their chronological age exhibited a substantial association with chronic ailments such as hypertension, hyperuricemia (characterised by high uric acid levels in the blood), and chronic obstructive pulmonary disease.

Consequently, the researchers concluded that individuals with higher AI-estimated ages were more likely to be afflicted by these aforementioned diseases.

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The researchers have posited that chest X-rays could serve as invaluable biomarkers for assessing ageing and longevity. Notably, these images offer more than just a visual representation of internal structures; they also provide insights into the intricacies of internal organs and bones.

(With inputs from agencies)

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