基于多模态影像的脑龄预测模型与应用
Brain Age Prediction Methods and Applications Based on Multimodal Neuroimaging Data
刘爽 1俞婧 2陈元园 1范秋筠 3赵欣 1明东4
作者信息
- 1. 天津大学医学工程与转化医学研究院,天津 300072;天津市脑科学中心,天津 300072
- 2. 天津大学医学工程与转化医学研究院,天津 300072
- 3. 天津市脑科学中心,天津 300072;天津大学精密仪器与光电子工程学院,天津 300072
- 4. 天津大学医学工程与转化医学研究院,天津 300072;天津市脑科学中心,天津 300072;天津大学精密仪器与光电子工程学院,天津 300072
- 折叠
摘要
脑龄预测是借助脑影像数据进行建模与分析,客观评估大脑成熟与老化程度的一种分析技术.随着人工智能算法的发展,脑龄预测的相关研究近年呈现快速增长态势.已有研究普遍认为脑龄预测有助于评估大脑的健康状态,是监测大脑异常发育和老化的有效指标,具有预测大脑异常老化和病变发生的巨大潜力.针对近年来脑龄预测领域的发展,从脑龄分类、脑龄模型及其临床应用等几方面,综述该领域的最新研究进展,并进一步概述脑龄研究未来发展的挑战和趋势.
Abstract
Brain age can predict the degree of brain maturity and aging by modeling and analysis of neuroimaging data.With the development of artificial intelligence algorithms,the related research on brain age prediction have demonstrated a rapid emerging trend.It is generally recognized that brain age can be an effective biomarker for monitoring abnormal development and aging,which can assess individual brain health,and has great potential to detect abnormal aging and disease.In the context of rapid growing of research interests on the brain age prediction,this review summarized the latest achievements from the aspects of brain age classification,brain age model,clinical application,and further discusses challenges and developing directions of brain age in the future studies.
关键词
神经影像/脑龄/脑龄预测偏差/深度学习Key words
neuroimaging data/brain age/predicted age difference/deep learning引用本文复制引用
基金项目
国家自然科学基金(81925020)
国家自然科学基金(82071994)
出版年
2024
NETL
NSTL
万方数据