Awang Mohd Khalid, Ali Ghulam, Faheem Muhammad
Faculty of Informatics and Computing Universiti Sultan Zainal Abidin (UniSZA) Terengganu Malaysia.
Department of Computer Science University of Okara Okara Pakistan.
Health Sci Rep. 2024 Sep 18;7(9):e70025. doi: 10.1002/hsr2.70025. eCollection 2024 Sep.
Alzheimer's disease (AD) is a degenerative neurological condition that worsens over time and leads to deterioration in cognitive abilities, reduced memory, and, eventually, a decrease in overall functioning. Timely and correct identification of Alzheimer's is essential for effective treatment. The systematic study specifically examines the application of deep learning (DL) algorithms in identifying AD using three-dimensional (3D) imaging methods. The main goal is to evaluate these methods' current state, efficiency, and potential enhancements, offering valuable insights into how DL could improve AD's rapid and precise diagnosis.
We searched different online repositories, such as IEEE Xplore, Elsevier, MDPI, PubMed Central, Science Direct, ACM, Springer, and others, to thoroughly summarize current research on DL methods to diagnose AD by analyzing 3D imaging data published between 2020 and 2024. We use PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to ensure the organization and understandability of the information collection process. We thoroughly analyzed the literature to determine the primary techniques used in these investigations and their findings.
The ability of convolutional neural networks (CNNs) and their variations, including 3D CNNs and recurrent neural networks, to detect both temporal and spatial characteristics in volumetric data has led to their widespread use. Methods such as transfer learning, combining multimodal data, and using attention procedures have improved models' precision and reliability. We selected 87 articles for evaluation. Out of these, 31 papers included various concepts, explanations, and elucidations of models and theories, while the other 56 papers primarily concentrated on issues related to practical implementation. This article introduces popular imaging types, 3D imaging for Alzheimer's detection, discusses the benefits and restrictions of the DL-based approach to AD assessment, and gives a view toward future developments resulting from critical evaluation.
阿尔茨海默病（AD）是一种退行性神经疾病，会随着时间的推移而恶化，导致认知能力下降、记忆力减退，并最终使整体功能衰退。及时、准确地识别阿尔茨海默病对于有效治疗至关重要。本系统研究具体考察了深度学习（DL）算法在使用三维（3D）成像方法识别AD中的应用。主要目标是评估这些方法的当前状态、效率和潜在改进，为DL如何改善AD的快速准确诊断提供有价值的见解。
我们搜索了不同的在线数据库，如IEEE Xplore、Elsevier、MDPI、PubMed Central、Science Direct、ACM、Springer等，通过分析2020年至2024年发表的关于通过3D成像数据诊断AD的DL方法的当前研究进行全面总结。我们使用PRISMA（系统评价和荟萃分析的首选报告项目）指南来确保信息收集过程的组织性和可理解性。我们对文献进行了全面分析，以确定这些研究中使用的主要技术及其结果。
卷积神经网络（CNN）及其变体，包括3D CNN和递归神经网络，在检测体数据中的时间和空间特征方面的能力导致了它们的广泛应用。迁移学习、结合多模态数据和使用注意力程序等方法提高了模型的精度和可靠性。我们选择了87篇文章进行评估。其中，31篇论文包含了模型和理论的各种概念、解释和说明，而其他56篇论文主要集中在与实际实现相关的问题上。本文介绍了常用的成像类型、用于阿尔茨海默病检测的3D成像，讨论了基于DL的AD评估方法的优点和局限性，并对批判性评估带来的未来发展给出了展望。
