脑老化
神经影像学
脑功能
认知
大脑结构与功能
大脑活动与冥想
大脑发育
脑解剖学
估计
机器学习
生物
人工智能
神经科学
计算机科学
磁共振成像
医学
脑电图
管理
放射科
经济
作者
Mohamed Azzam,Ziyang Xu,Ruobing Liu,Lie Li,Kah Meng Soh,Kishore B. Challagundla,Shibiao Wan,Jieqiong Wang
摘要
Abstract The study of brain age has emerged over the past decade, aiming to estimate a person’s age based on brain imaging scans. Ideally, predicted brain age should match chronological age in healthy individuals. However, brain structure and function change in the presence of brain-related diseases. Consequently, brain age also changes in affected individuals, making the brain age gap (BAG)—the difference between brain age and chronological age—a potential biomarker for brain health, early screening, and identifying age-related cognitive decline and disorders. With the recent successes of artificial intelligence in healthcare, it is essential to track the latest advancements and highlight promising directions. This review paper presents recent machine learning techniques used in brain age estimation (BAE) studies. Typically, BAE models involve developing a machine learning regression model to capture age-related variations in brain structure from imaging scans of healthy individuals and automatically predict brain age for new subjects. The process also involves estimating BAG as a measure of brain health. While we discuss recent clinical applications of BAE methods, we also review studies of biological age that can be integrated into BAE research. Finally, we point out the current limitations of BAE’s studies.
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