[1]廖建灏,吴凯,黄家源,等.深度学习算法在脑年龄预测中的应用进展[J].中国医学物理学杂志,2025,42(1):122-127.[doi:DOI:10.3969/j.issn.1005-202X.2025.01.016]
　LIAO Jianhao,WU Kai,,et al.Advances in deep learning algorithms for brain age prediction[J].Chinese Journal of Medical Physics,2025,42(1):122-127.[doi:DOI:10.3969/j.issn.1005-202X.2025.01.016]
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深度学习算法在脑年龄预测中的应用进展()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第1期
页码:
122-127
栏目:
医学人工智能
出版日期:
2025-01-19

文章信息/Info

Title:
Advances in deep learning algorithms for brain age prediction
文章编号:
1005-202X（2025）01-0122-06
作者:
廖建灏1; 吴凯1; 3; 4; 5; 6; 黄家源1; 韩睿1; 彭润霖1; 周静2; 3; 4; 5
1.华南理工大学生物医学科学与工程学院， 广东 广州 511442； 2.华南理工大学材料科学与工程学院， 广东 广州 510006； 3.华南理工大学国家人体组织功能重建工程技术研究中心， 广东 广州 510006； 4.广东省精神疾病转化医学工程技术研究中心， 广东 广州 510370； 5.广东省老年痴呆诊断与康复工程技术研究中心， 广东 广州 510500； 6.华南理工大学广东省生物医学工程重点实验室， 广东 广州 510006
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
关键词:
脑年龄; 机器学习; 深度学习; 回归预测; 综述
Keywords:
Keywords: brain age machine learning deep learning regression prediction review
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2025.01.016
文献标志码:
A
摘要:
脑年龄预测的研究对于深入理解个体神经发育、神经精神性疾病的早期诊断以及制定个性化治疗方案具有重要意义，随着深度学习的不断发展，越来越多的研究专注于利用此类算法来预测脑年龄。相较于传统回归算法，深度学习具备复杂模式学习、端到端学习和高度自适应性等优势，能更准确地揭示神经精神疾病的神经病理机制，为临床评估、辅助诊断以及预后预测提供更为精准的工具。本综述提供了近年来深度学习算法在脑年龄预测研究方面的应用进展，介绍了在脑年龄预测中深度学习模型的改进、多模态数据输入和可解释性研究上的进展，最后讨论了集成深度学习架构的建立方法和制定统一的基准测试的未来挑战，并展望了深度学习在脑年龄预测中的应用前景。
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.

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备注/Memo

备注/Memo:
【收稿日期】2024-10-20 【基金项目】国家重点研发计划（2023YFC2414500, 2023YFC2414504）；广东省基础与应用基础研究基金杰出青年项目（2021B1515020064）；国家自然科学基金（72174082, 82271953, 81971585, 82301688）；广东省基础与应用基础研究基金 (2022A1515140142)；广州市科技计划（202206060005, 202206080005, 202206010077, 202206010034, 2023A03J0856, 2023A03J0839） 【作者简介】廖建灏，研究方向：生物医学信号处理、医学人工智能，E-mail: 1009813948@qq.com 【通信作者】周静，博士，研究方向：生物医学信号处理、医学人工智能，E-mail: hellozj@scut.edu.cn
更新日期/Last Update: 2025-01-19