Researchers from Osaka Capital University have advanced artificial intelligence to assess biological age using chest radiographs, a finding reported in Lancet Healthy Longevity. In a landmark effort, clinicians and data scientists combined large-scale imaging with deep learning to infer biological aging from routine chest X-rays, offering a potential noninvasive biomarker for health status. The work represents a new chapter in radiology where machine learning augments clinical insight, enabling physicians to gauge an individual’s biological age alongside traditional clinical measures.
For the first time, a deep learning–based AI model has been trained to estimate age from chest X-rays, and the study offers a detailed look at how the algorithm was developed, trained, and evaluated. A substantial dataset comprising 67,099 radiographs from 36,051 healthy individuals collected between 2008 and 2021 formed the backbone of the training process. These images were gathered as part of routine medical examinations conducted across three hospitals, reflecting real-world imaging practices in diverse patient populations across Canada, the United States, and beyond. The researchers emphasize that the radiographs used for training captured a wide spectrum of ages, body habitus, and imaging workflows, bolstering the model’s generalizability to varied clinical settings common in North American healthcare systems.
The model was subsequently tested on chest radiographs from patients presenting with various health conditions. To evaluate its robustness, additional imaging data were assembled from 34,197 patients with hypertension, metabolic disorders, and chronic obstructive pulmonary disease. This validation step explored how disease states might influence perceived biological age and whether the algorithm could maintain accuracy in the presence of comorbidities more prevalent in aging populations within North America.
During evaluation, the AI showed a tendency to overestimate biological age in patients with certain illnesses, underscoring the complexity of disentangling aging signals from disease-related changes on radiographs. This nuance highlights the need for careful calibration when applying age estimates to individuals with chronic conditions, as misinterpretation could affect risk stratification and clinical decision-making. Nonetheless, the researchers observed that the model could still correlate with clinical health indicators in substantial ways, suggesting that radiographic age estimates may reflect overall physiological reserve and underlying health status rather than simply anatomical age alone.
Authors of the study contend that biologically informed age estimates derived from chest X-rays could become a valuable tool for assessing disease severity, projecting life expectancy, and predicting possible surgical complications. In practice, such metrics could complement existing risk models used by surgeons and internists, aiding in preoperative planning, monitoring disease progression, and tailoring interventions to individual patients. The potential applications extend to population health research, where imaging-derived aging signals might illuminate trends in chronic disease burden, treatment response, and longevity, particularly in populations with high rates of cardiometabolic disorders examined in North American healthcare settings.
As clinical teams in North America begin to explore such imaging biomarkers, the work from Osaka Capital University offers a compelling blueprint for integrating AI-driven age estimation into routine radiology workflows. The approach demonstrates how large-scale, multi-institutional data can yield scalable models that support clinicians in interpreting radiographic aging signals in the context of a patient’s broader health profile, comorbidity history, and functional status. This line of inquiry holds promise for refining prognostic assessments, guiding preventive strategies, and potentially informing personalized care pathways for aging populations in Canada and the United States.
In closing, the study represents a significant step toward harnessing radiographic data to illuminate biological aging, with clear implications for chronic disease management, risk stratification, and surgical planning. While additional refinements are needed to address disease-related biases and to validate predictive value across diverse populations, the research establishes a solid foundation for future AI tools that quantify health status from standard imaging, leveraging the vast troves of X-ray data collected in modern medical systems.
The overarching message from the researchers is that chest X-ray–based age estimation could become a practical, noninvasive proxy for health status, provided that models are calibrated to account for individual disease profiles and demographic differences across populations in North America. This evolving field invites clinicians to rethink how imaging can contribute to personalized risk assessment and outcomes forecasting, ultimately supporting better-informed clinical decisions and improved patient care across Canada and the United States.