School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran.
School of Computer Science, Institute for Research in Fundamental Science (IPM), P.O. Box 19395-5746, Tehran, Iran.
J Med Syst. 2019 Jul 11;43(8):279. doi: 10.1007/s10916-019-1401-7.
Human age prediction is an interesting and applicable issue in different fields. It can be based on various criteria such as face image, DNA methylation, chest plate radiographs, knee radiographs, dental images and etc. Most of the age prediction researches have mainly been based on images. Since the image processing and Machine Learning (ML) techniques have grown up, the investigations were led to use them in age prediction problem. The implementations would be used in different fields, especially in medical applications. Brain Age Estimation (BAE) has attracted more attention in recent years and it would be so helpful in early diagnosis of some neurodegenerative diseases such as Alzheimer, Parkinson, Huntington, etc. BAE is performed on Magnetic Resonance Imaging (MRI) images to compute the brain ages. Studies based on brain MRI shows that there is a relation between accelerated aging and accelerated brain atrophy. This refers to the effects of neurodegenerative diseases on brain structure while making the whole of it older. This paper reviews and summarizes the main approaches for age prediction based on brain MRI images including preprocessing methods, useful tools used in different research works and the estimation algorithms. We categorize the BAE methods based on two factors, first the way of processing MRI images, which includes pixel-based, surface-based, or voxel-based methods and second, the generation of ML algorithms that includes traditional or Deep Learning (DL) methods. The modern techniques as DL methods help MRI based age prediction to get results that are more accurate. In recent years, more precise and statistical ML approaches have been utilized with the help of related tools for simplifying computations and getting accurate results. Pros and cons of each research and the challenges in each work are expressed and some guidelines and deliberations for future research are suggested.
人类年龄预测在不同领域是一个有趣且适用的问题。它可以基于各种标准，如面部图像、DNA 甲基化、胸板射线照片、膝关节射线照片、牙齿图像等。大多数年龄预测研究主要基于图像。随着图像处理和机器学习（ML）技术的发展，研究人员开始将这些技术应用于年龄预测问题。这些方法将应用于不同领域，特别是在医学应用中。脑年龄估计（BAE）近年来引起了更多关注，它将有助于阿尔茨海默病、帕金森病、亨廷顿病等一些神经退行性疾病的早期诊断。BAE 是在磁共振成像（MRI）图像上进行的，以计算脑龄。基于脑 MRI 的研究表明，衰老加速与脑萎缩加速之间存在关系。这是指神经退行性疾病对大脑结构的影响，使整个大脑变得更老。本文综述和总结了基于脑 MRI 图像的年龄预测的主要方法，包括预处理方法、不同研究工作中使用的有用工具和估计算法。我们根据两个因素对 BAE 方法进行分类，首先是 MRI 图像的处理方式，包括基于像素、基于表面或基于体素的方法，其次是 ML 算法的生成，包括传统或深度学习（DL）方法。DL 等现代技术有助于更准确地进行基于 MRI 的年龄预测。近年来，借助相关工具简化计算并获得准确结果，更多精确和统计的 ML 方法得到了利用。表达了每个研究的优缺点以及每个工作中的挑战，并为未来的研究提出了一些指导方针和讨论。
