Fridolin Haugg, Grace Lee, John He, Justin Johnson, Anna Zapaishchykova, Danielle S Bitterman, Benjamin H Kann, Hugo J W L Aerts, Raymond H Mak
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引用次数: 0
Abstract
Chronological age, although commonly used in clinical practice, fails to capture individual variations in rates of ageing and physiological decline. Recent advances in artificial intelligence (AI) have transformed the estimation of biological age using various imaging techniques. This Review consolidates AI developments in age prediction across brain, chest, abdominal, bone, and facial imaging using diverse methods, including MRI, CT, x-ray, and photographs. The difference between predicted and chronological age-often referred to as age deviation-is a promising biomarker for assessing health status and predicting disease risk. In this Review, we highlight consistent associations between age deviation and various health outcomes, including mortality risk, cognitive decline, and cardiovascular prognosis. We also discuss the technical challenges in developing unbiased models and ethical considerations for clinical application. This Review highlights the potential of AI-based age estimation in personalised medicine as it offers a non-invasive, interpretable biomarker that could transform health risk assessment and guide preventive interventions.
期刊介绍:
The Lancet Healthy Longevity, a gold open-access journal, focuses on clinically-relevant longevity and healthy aging research. It covers early-stage clinical research on aging mechanisms, epidemiological studies, and societal research on changing populations. The journal includes clinical trials across disciplines, particularly in gerontology and age-specific clinical guidelines. In line with the Lancet family tradition, it advocates for the rights of all to healthy lives, emphasizing original research likely to impact clinical practice or thinking. Clinical and policy reviews also contribute to shaping the discourse in this rapidly growing discipline.