CSF Biomarkers for Early Diagnosis of Synucleinopathies: Focus on Idiopathic RBD

Liguori, Claudio; Paoletti, Federico Paolini; Placidi, Fabio; Ruffini, Roberta; Sancesario, Giulia Maria; Mercuri, Nicola Biagio; Parnetti, Lucilla

doi:10.1007/s11910-019-0918-y

Cited by 13 publications

(1 citation statement)

References 52 publications

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“…There are diagnostic methods based on biomarker detection; cerebrospinal fluid (CSF) biomarkers Aβ 1-42 , T-tau, and P-tau are well-validated, and are being increasingly used in clinical practice as tools for the affirmative diagnosis of AD [ 41 ]. The long-term stability of core CSF biomarkers in patients with AD provides further support for their use in clinical studies and treatment monitoring in clinical trials [ 42 ]. Michael E. et al [ 43 ] proposed that CSF Aβ 1-42 showed the best diagnostic accuracy among the CSF biomarkers.…”

Section: Related Workmentioning

confidence: 99%

An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer’s Disease

Sun

Wang

Liu

2021

Sensors

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The early diagnosis of Alzheimer’s disease (AD) can allow patients to take preventive measures before irreversible brain damage occurs. It can be seen from cross-sectional imaging studies of AD that the features of the lesion areas in AD patients, as observed by magnetic resonance imaging (MRI), show significant variation, and these features are distributed throughout the image space. Since the convolutional layer of the general convolutional neural network (CNN) cannot satisfactorily extract long-distance correlation in the feature space, a deep residual network (ResNet) model, based on spatial transformer networks (STN) and the non-local attention mechanism, is proposed in this study for the early diagnosis of AD. In this ResNet model, a new Mish activation function is selected in the ResNet-50 backbone to replace the Relu function, STN is introduced between the input layer and the improved ResNet-50 backbone, and a non-local attention mechanism is introduced between the fourth and the fifth stages of the improved ResNet-50 backbone. This ResNet model can extract more information from the layers by deepening the network structure through deep ResNet. The introduced STN can transform the spatial information in MRI images of Alzheimer’s patients into another space and retain the key information. The introduced non-local attention mechanism can find the relationship between the lesion areas and normal areas in the feature space. This model can solve the problem of local information loss in traditional CNN and can extract the long-distance correlation in feature space. The proposed method was validated using the ADNI (Alzheimer’s disease neuroimaging initiative) experimental dataset, and compared with several models. The experimental results show that the classification accuracy of the algorithm proposed in this study can reach 97.1%, the macro precision can reach 95.5%, the macro recall can reach 95.3%, and the macro F1 value can reach 95.4%. The proposed model is more effective than other algorithms.

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Section: Related Workmentioning

confidence: 99%