Advances in deep learning algorithms for brain age prediction
10.3969/j.issn.1005-202X.2025.01.016
- VernacularTitle:深度学习算法在脑年龄预测中的应用进展
- Author:
Jianhao LIAO
1
;
Kai WU
;
Jiayuan HUANG
;
Rui HAN
;
Runlin PENG
;
Jing ZHOU
Author Information
1. 华南理工大学生物医学科学与工程学院,广东广州511442
- Publication Type:Journal Article
- Keywords:
brain age;
machine learning;
deep learning;
regression prediction;
review
- From:
Chinese Journal of Medical Physics
2025;42(1):122-127
- CountryChina
- Language:Chinese
-
Abstract:
Brain age prediction is of great significance to the in-depth understanding of individual neurodevelopment,early diagnosis of neuropsychiatric disorders,and formulation of personalized treatment plans. With the continuous advancement of deep learning,more and more researches focus on using such algorithms to predict brain age. Compared with traditional regression algorithms,deep learning which has the advantages of complex pattern learning,end-to-end learning and high adaptability can more accurately reveal the neuropathological mechanisms of neuropsychiatric disorders,and provide more precise tools for clinical assessment,assisted diagnosis and prognosis prediction. Herein the study reviews the recent advances in the application of deep learning algorithms in brain age prediction,introduces the achievements in deep learning model optimization,multimodal data inputs and interpretability studies for brain age prediction,discusses the methods for the establishment of integrated deep learning architectures and the future challenges of developing unified benchmarking,and provides an outlook on the application of deep learning in brain age prediction.
