Alzheimer’s disease (AD) is a degenerative disorder that leads to progressive, irreversible cognitive decline. To obtain an accurate and timely diagnosis and detect AD at an early stage, numerous approaches based on convolutional neural networks (CNNs) using neuroimaging data have been proposed. Because 3D CNNs can extract more spatial discrimination information than 2D CNNs, they have emerged as a promising research direction in the diagnosis of AD. The aim of this article is to present the current state of the art in the diagnosis of AD using 3D CNN models and neuroimaging modalities, focusing on the 3D CNN architectures and classification methods used, and to highlight potential future research topics. To give the reader a better overview of the content mentioned in this review, we briefly introduce the commonly used imaging datasets and the fundamentals of CNN architectures. Then we carefully analyzed the existing studies on AD diagnosis, which are divided into two levels according to their inputs: 3D subject-level CNNs and 3D patch-level CNNs, highlighting their contributions and significance in the field. In addition, this review discusses the key findings and challenges from the studies and highlights the lessons learned as a roadmap for future research. Finally, we summarize the paper by presenting some major findings, identifying open research challenges, and pointing out future research directions.

中文翻译：

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三维卷积神经网络在神经影像诊断阿尔茨海默病中的应用综述

阿尔茨海默病 (AD) 是一种退行性疾病，会导致进行性、不可逆转的认知能力下降。为了获得准确、及时的诊断并在早期检测 AD，人们提出了许多基于使用神经影像数据的卷积神经网络 (CNN) 的方法。由于 3D CNN 比 2D CNN 可以提取更多的空间辨别信息，因此它们已成为 AD 诊断中一个有前途的研究方向。本文的目的是介绍使用 3D CNN 模型和神经影像模式诊断 AD 的最新技术，重点关注所使用的 3D CNN 架构和分类方法，并强调未来潜在的研究主题。为了让读者更好地了解本综述中提到的内容，我们简要介绍了常用的成像数据集和 CNN 架构的基础知识。然后我们仔细分析了现有的 AD 诊断研究，根据输入分为两个级别：3D 主题级 CNN 和 3D 块级 CNN，强调它们在该领域的贡献和意义。此外，本综述讨论了研究的主要发现和挑战，并强调了吸取的经验教训，作为未来研究的路线图。最后，我们通过提出一些主要发现、确定开放研究挑战并指出未来的研究方向来总结本文。