Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China.
Tomography. 2024 Aug 12;10(8):1238-1262. doi: 10.3390/tomography10080093.
The concept of 'brain age', derived from neuroimaging data, serves as a crucial biomarker reflecting cognitive vitality and neurodegenerative trajectories. In the past decade, machine learning (ML) and deep learning (DL) integration has transformed the field, providing advanced models for brain age estimation. However, achieving precise brain age prediction across all ages remains a significant analytical challenge. This comprehensive review scrutinizes advancements in ML- and DL-based brain age prediction, analyzing 52 peer-reviewed studies from 2020 to 2024. It assesses various model architectures, highlighting their effectiveness and nuances in lifespan brain age studies. By comparing ML and DL, strengths in forecasting and methodological limitations are revealed. Finally, key findings from the reviewed articles are summarized and a number of major issues related to ML/DL-based lifespan brain age prediction are discussed. Through this study, we aim at the synthesis of the current state of brain age prediction, emphasizing both advancements and persistent challenges, guiding future research, technological advancements, and improving early intervention strategies for neurodegenerative diseases.
“大脑年龄”的概念源自神经影像学数据，它是反映认知活力和神经退行性轨迹的关键生物标志物。在过去十年中，机器学习 (ML) 和深度学习 (DL) 的融合改变了这一领域，为大脑年龄估计提供了先进的模型。然而，实现所有年龄段的精确大脑年龄预测仍然是一个重大的分析挑战。本综述全面审查了基于 ML 和 DL 的大脑年龄预测的进展，分析了 2020 年至 2024 年的 52 篇同行评审研究。它评估了各种模型架构，强调了它们在寿命大脑年龄研究中的有效性和细微差别。通过比较 ML 和 DL，揭示了预测的优势和方法学的局限性。最后，总结了综述文章的主要发现，并讨论了与基于 ML/DL 的寿命大脑年龄预测相关的几个主要问题。通过这项研究，我们旨在综合大脑年龄预测的现状，强调进展和持续存在的挑战，指导未来的研究、技术进步，并改善神经退行性疾病的早期干预策略。
