Imaging biomarkers of ageing: a review of artificial intelligence-based approaches for age estimation.
Authors
Affiliations (4)
Affiliations (4)
- Department of Radiation Oncology, Mass General Brigham, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
- Department of Radiation Oncology, Mass General Brigham, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Department of Radiation Oncology, Mass General Brigham, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA; Radiology and Nuclear Medicine, CARIM and GROW, Maastricht University, Maastricht, Netherlands; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: [email protected].
- Department of Radiation Oncology, Mass General Brigham, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA. Electronic address: [email protected].
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.