Department of Information Technology, University Institute of Engineering & Technology, Panjab University, Chandigarh 160014, India.
Department of Information Technology, University Institute of Engineering & Technology, Panjab University, Chandigarh 160014, India.
Neurosci Lett. 2023 Nov 20;817:137530. doi: 10.1016/j.neulet.2023.137530. Epub 2023 Oct 17.
The aim of this study is to develop a deep neural network to diagnosis Alzheimer's disease and categorize the stages of the disease using FDG-PET scans. Fluorodeoxyglucose positron emission tomography (FDG-PET) is a highly effective diagnostic tool that accurately detects glucose metabolism in the brain of AD patients.
In this work, we have developed a deep neural network using FDG-PET to discriminate Alzheimer's disease subjects from stable mild cognitive impairment (sMCI), progressive mild cognitive impairment (pMCI), and cognitively normal (CN) cohorts. A total of 83 FDG-PET scans are collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 21 subjects with CN, 21 subjects with sMCI, 21 subjects with pMCI, and 20 subjects with AD.
The method has achieved remarkable accuracy rates of 99.31% for CN vs. AD, 99.88% for CN vs. MCI, 99.54% for AD vs. MCI, and 96.81% for pMCI vs. sMCI. Based on the experimental results.
The results show that the proposed method has a significant generalisation ability as well as good performance in predicting the conversion of MCI to AD even in the absence of direct information. FDG-PET is a well-known biomarker for the identification of Alzheimer's disease using transfer learning.
本研究旨在开发一种深度神经网络，使用 FDG-PET 扫描来诊断阿尔茨海默病并对疾病进行分期。氟代脱氧葡萄糖正电子发射断层扫描（FDG-PET）是一种非常有效的诊断工具，可准确检测 AD 患者大脑中的葡萄糖代谢。
在这项工作中，我们使用 FDG-PET 开发了一种深度神经网络，用于区分阿尔茨海默病患者与稳定轻度认知障碍（sMCI）、进行性轻度认知障碍（pMCI）和认知正常（CN）队列。共从阿尔茨海默病神经影像学倡议（ADNI）数据库中收集了 83 个 FDG-PET 扫描，包括 21 名 CN 受试者、21 名 sMCI 受试者、21 名 pMCI 受试者和 20 名 AD 受试者。
该方法在 CN 与 AD 之间的准确率达到了 99.31%，在 CN 与 MCI 之间的准确率达到了 99.88%，在 AD 与 MCI 之间的准确率达到了 99.54%，在 pMCI 与 sMCI 之间的准确率达到了 96.81%。基于实验结果。
结果表明，该方法具有显著的泛化能力和良好的性能，即使在没有直接信息的情况下，也可以预测 MCI 向 AD 的转化。FDG-PET 是一种通过迁移学习识别阿尔茨海默病的已知生物标志物。
