Khatri Uttam, Kwon Goo-Rak
Department of Information and Communication Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Republic of Korea.
Bioengineering (Basel). 2023 Oct 20;10(10):1225. doi: 10.3390/bioengineering10101225.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide. Early and accurate prediction of AD progression is crucial for early intervention and personalized treatment planning. Although AD does not yet have a reliable therapy, several medications help slow down the disease's progression. However, more study is still needed to develop reliable methods for detecting AD and its phases. In the recent past, biomarkers associated with AD have been identified using neuroimaging methods. To uncover biomarkers, deep learning techniques have quickly emerged as a crucial methodology. A functional molecular imaging technique known as fluorodeoxyglucose positron emission tomography (18F-FDG-PET) has been shown to be effective in assisting researchers in understanding the morphological and neurological alterations to the brain associated with AD. Convolutional neural networks (CNNs) have also long dominated the field of AD progression and have been the subject of substantial research, while more recent approaches like vision transformers (ViT) have not yet been fully investigated. In this paper, we present a self-supervised learning (SSL) method to automatically acquire meaningful AD characteristics using the ViT architecture by pretraining the feature extractor using the self-distillation with no labels (DINO) and extreme learning machine (ELM) as classifier models. In this work, we examined a technique for predicting mild cognitive impairment (MCI) to AD utilizing an SSL model which learns powerful representations from unlabeled 18F-FDG PET images, thus reducing the need for large-labeled datasets. In comparison to several earlier approaches, our strategy showed state-of-the-art classification performance in terms of accuracy (92.31%), specificity (90.21%), and sensitivity (95.50%). Then, to make the suggested model easier to understand, we highlighted the brain regions that significantly influence the prediction of MCI development. Our methods offer a precise and efficient strategy for predicting the transition from MCI to AD. In conclusion, this research presents a novel Explainable SSL-ViT model that can accurately predict AD progress based on 18F-FDG PET scans. SSL, attention, and ELM mechanisms are integrated into the model to make it more predictive and interpretable. Future research will enable the development of viable treatments for neurodegenerative disorders by combining brain areas contributing to projection with observed anatomical traits.
阿尔茨海默病（AD）是一种进行性神经退行性疾病，影响着全球数百万人。对AD进展进行早期准确预测对于早期干预和个性化治疗规划至关重要。尽管AD尚无可靠的治疗方法，但有几种药物有助于减缓疾病进展。然而，仍需要更多研究来开发检测AD及其阶段的可靠方法。最近，已使用神经成像方法鉴定了与AD相关的生物标志物。为了发现生物标志物，深度学习技术迅速成为一种关键方法。一种称为氟脱氧葡萄糖正电子发射断层扫描（18F-FDG-PET）的功能分子成像技术已被证明可有效帮助研究人员了解与AD相关的大脑形态和神经学改变。卷积神经网络（CNN）长期以来也在AD进展领域占据主导地位，并且一直是大量研究的主题，而像视觉Transformer（ViT）这样的最新方法尚未得到充分研究。在本文中，我们提出一种自监督学习（SSL）方法，通过使用无标签自蒸馏（DINO）和极限学习机（ELM）作为分类器模型对特征提取器进行预训练，利用ViT架构自动获取有意义的AD特征。在这项工作中，我们研究了一种利用SSL模型预测轻度认知障碍（MCI）向AD转变的技术，该模型从未标记的18F-FDG PET图像中学习强大的表征，从而减少了对大量标记数据集的需求。与几种早期方法相比，我们的策略在准确率（92.31%）、特异性（90.21%）和灵敏度（95.50%）方面表现出了领先的分类性能。然后，为了使所提出的模型更易于理解，我们突出了对MCI发展预测有显著影响的脑区。我们的方法为预测从MCI向AD的转变提供了一种精确且高效的策略。总之，本研究提出了一种新颖的可解释SSL-ViT模型，该模型可以基于18F-FDG PET扫描准确预测AD进展。SSL、注意力和ELM机制被集成到模型中，使其更具预测性和可解释性。未来的研究将通过结合对投影有贡献的脑区与观察到的解剖特征，推动神经退行性疾病可行治疗方法的开发。
