Abstract

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Author(s):: LIAO Jianhao1; WU Kai1; 3; 4; 5; 6; HUANG Jiayuan1; HAN Rui1; PENG Runlin1; ZHOU Jing2; 3; 4; 5; 1. School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou 511442, China 2. School of Materials Science and Engineering, South China University of Technology, Guangzhou 510006, China 3. National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China 4. Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China 5. Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou 510500, China 6. Key Laboratory of Biomedical Engineering of Guangdong Province, South China University of Technology, Guangzhou 510006, China

Keywords:: Keywords: brain age machine learning deep learning regression prediction review

Abstract:: 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.