Sarma Manash, Chatterjee Subarna
Computer Science and Engineering, Faculty of Engineering and Technology, Technology Campus (Peenya Campus), Ramaiah University of Applied Sciences, Bengaluru 560058, India.
Diagnostics (Basel). 2024 Nov 23;14(23):2640. doi: 10.3390/diagnostics14232640.
Late-onset Alzheimer's disease (LOAD) is a subtype of dementia that manifests after the age of 65. It is characterized by progressive impairments in cognitive functions, behavioral changes, and learning difficulties. Given the progressive nature of the disease, early diagnosis is crucial. Early-onset Alzheimer's disease (EOAD) is solely attributable to genetic factors, whereas LOAD has multiple contributing factors. A complex pathway mechanism involving multiple factors contributes to LOAD progression. Employing a systems biology approach, our analysis encompassed the genetic, epigenetic, metabolic, and environmental factors that modulate the molecular networks and pathways. These factors affect the brain's structural integrity, functional capacity, and connectivity, ultimately leading to the manifestation of the disease. This study has aggregated diverse biomarkers associated with factors capable of altering the molecular networks and pathways that influence brain structure, functionality, and connectivity. These biomarkers serve as potential early indicators for AD diagnosis and are designated as early biomarkers. The other biomarker datasets associated with the brain structure, functionality, connectivity, and related parameters of an individual are broadly categorized as clinical-stage biomarkers. This study has compiled research papers on Alzheimer's disease (AD) diagnosis utilizing machine learning (ML) methodologies from both categories of biomarker data, including the applications of ML techniques for AD diagnosis. The broad objectives of our study are research gap identification, assessment of biomarker efficacy, and the most effective or prevalent ML technology used in AD diagnosis. This paper examines the predominant use of deep learning (DL) and convolutional neural networks (CNNs) in Alzheimer's disease (AD) diagnosis utilizing various types of biomarker data. Furthermore, this study has addressed the potential scope of using generative AI and the Synthetic Minority Oversampling Technique (SMOTE) for data augmentation.
晚发性阿尔茨海默病（LOAD）是一种在65岁之后出现症状的痴呆亚型。其特征为认知功能逐渐受损、行为改变以及学习困难。鉴于该疾病的渐进性，早期诊断至关重要。早发性阿尔茨海默病（EOAD）完全由遗传因素导致，而LOAD则有多种促成因素。涉及多个因素的复杂通路机制促成了LOAD的进展。我们采用系统生物学方法进行分析，涵盖了调节分子网络和通路的遗传、表观遗传、代谢及环境因素。这些因素影响大脑的结构完整性、功能能力和连通性，最终导致疾病的显现。本研究汇总了与能够改变影响大脑结构、功能和连通性的分子网络及通路的因素相关的多种生物标志物。这些生物标志物可作为AD诊断的潜在早期指标，被称为早期生物标志物。与个体大脑结构、功能、连通性及相关参数相关的其他生物标志物数据集大致归类为临床阶段生物标志物。本研究汇编了利用这两类生物标志物数据中的机器学习（ML）方法进行阿尔茨海默病（AD）诊断的研究论文，包括ML技术在AD诊断中的应用。我们研究的广泛目标是识别研究差距、评估生物标志物功效以及AD诊断中使用的最有效或最普遍的ML技术。本文探讨了深度学习（DL）和卷积神经网络（CNN）在利用各类生物标志物数据进行阿尔茨海默病（AD）诊断中的主要应用。此外，本研究还探讨了使用生成式人工智能和合成少数过采样技术（SMOTE）进行数据增强的潜在范围。
