scholarly journals Deep Learning-Based Classification and Voxel-Based Visualization of Frontotemporal Dementia and Alzheimer’s Disease

2021 ◽  
Vol 14 ◽  
Author(s):  
Jingjing Hu ◽  
Zhao Qing ◽  
Renyuan Liu ◽  
Xin Zhang ◽  
Pin Lv ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differential Diagnosis ◽  
Deep Learning ◽  
Frontotemporal Dementia ◽  
Image Data ◽  
Rapid Acquisition ◽  
Magnetic Resonance Imaging Mri ◽  
The One ◽  
The Right

Frontotemporal dementia (FTD) and Alzheimer’s disease (AD) have overlapping symptoms, and accurate differential diagnosis is important for targeted intervention and treatment. Previous studies suggest that the deep learning (DL) techniques have the potential to solve the differential diagnosis problem of FTD, AD and normal controls (NCs), but its performance is still unclear. In addition, existing DL-assisted diagnostic studies still rely on hypothesis-based expert-level preprocessing. On the one hand, it imposes high requirements on clinicians and data themselves; On the other hand, it hinders the backtracking of classification results to the original image data, resulting in the classification results cannot be interpreted intuitively. In the current study, a large cohort of 3D T1-weighted structural magnetic resonance imaging (MRI) volumes (n = 4,099) was collected from two publicly available databases, i.e., the ADNI and the NIFD. We trained a DL-based network directly based on raw T1 images to classify FTD, AD and corresponding NCs. And we evaluated the convergence speed, differential diagnosis ability, robustness and generalizability under nine scenarios. The proposed network yielded an accuracy of 91.83% based on the most common T1-weighted sequence [magnetization-prepared rapid acquisition with gradient echo (MPRAGE)]. The knowledge learned by the DL network through multiple classification tasks can also be used to solve subproblems, and the knowledge is generalizable and not limited to a specified dataset. Furthermore, we applied a gradient visualization algorithm based on guided backpropagation to calculate the contribution graph, which tells us intuitively why the DL-based networks make each decision. The regions making valuable contributions to FTD were more widespread in the right frontal white matter regions, while the left temporal, bilateral inferior frontal and parahippocampal regions were contributors to the classification of AD. Our results demonstrated that DL-based networks have the ability to solve the enigma of differential diagnosis of diseases without any hypothesis-based preprocessing. Moreover, they may mine the potential patterns that may be different from human clinicians, which may provide new insight into the understanding of FTD and AD.

scholarly journals The Diagnosis of Alzheimer’s Disease: An Ensemble Approach

2020 ◽  
Author(s):  
Jingyan Qiu ◽  
Linjian Li ◽  
Yida Liu ◽  
Yingjun Ou ◽  
Yubei Lin
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Large Scale ◽  
Early Stage ◽  
Training Transfer ◽  
Detection And Diagnosis ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Learning Model ◽  
The Brain

Alzheimer’s disease (AD) is one of the most common forms of dementia. The early stage of the disease is defined as Mild Cognitive Impairment (MCI). Recent research results have shown the prospect of combining Magnetic Resonance Imaging (MRI) scanning of the brain and deep learning to diagnose AD. However, the CNN deep learning model requires a large scale of samples for training. Transfer learning is the key to enable a model with high accuracy by using limited data for training. In this paper, DenseNet and Inception V4, which were pre-trained on the ImageNet dataset to obtain initialization values of weights, are, respectively, used for the graphic classification task. The ensemble method is employed to enhance the effectiveness and efficiency of the classification models and the result of different models are eventually processed through probability-based fusion. Our experiments were completely conducted on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) public dataset. Only the ternary classification is made due to a higher demand for medical detection and diagnosis. The accuracies of AD/MCI/Normal Control (NC) of different models are estimated in this paper. The results of the experiments showed that the accuracies of the method achieved a maximum of 92.65%, which is a remarkable outcome compared with the accuracies of the state-of-the-art methods.

Brazilian Version of Addenbrooke’s Cognitive Examination—Revised in the Differential Diagnosis of Alzheimer’S Disease and Behavioral Variant Frontotemporal Dementia

2021 ◽  
Author(s):  
Viviane Amaral-Carvalho ◽  
Thais Bento Lima-Silva ◽  
Luciano Inácio Mariano ◽  
Leonardo Cruz de Souza ◽  
Henrique Cerqueira Guimarães ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differential Diagnosis ◽  
Diagnostic Accuracy ◽  
Frontotemporal Dementia ◽  
Predictive Value ◽  
Univariate Analysis ◽  
Behavioral Variant Frontotemporal Dementia ◽  
Present Sample ◽  
Addenbrooke’S Cognitive Examination

Abstract Introduction Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are frequent causes of dementia and, therefore, instruments for differential diagnosis between these two conditions are of great relevance. Objective To investigate the diagnostic accuracy of Addenbrooke’s Cognitive Examination-Revised (ACE-R) for differentiating AD from bvFTD in a Brazilian sample. Methods The ACE-R was administered to 102 patients who had been diagnosed with mild dementia due to probable AD, 37 with mild bvFTD and 161 cognitively healthy controls, matched according to age and education. Additionally, all subjects were assessed using the Mattis Dementia Rating Scale and the Neuropsychiatric Inventory. The performance of patients and controls was compared by using univariate analysis, and ROC curves were calculated to investigate the accuracy of ACE-R for differentiating AD from bvFTD and for differentiating AD and bvFTD from controls. The verbal fluency plus language to orientation plus name and address delayed recall memory (VLOM) ratio was also calculated. Results The optimum cutoff scores for ACE-R were <80 for AD, <79 for bvFTD, and <80 for dementia (AD + bvFTD), with area under the receiver operating characteristic curves (ROC) (AUC) >0.85. For the differential diagnosis between AD and bvFTD, a VLOM ratio of 3.05 showed an AUC of 0.816 (Cohen’s d = 1.151; p < .001), with 86.5% sensitivity, 71.4% specificity, 72.7% positive predictive value, and 85.7% negative predictive value. Conclusions The Brazilian ACE-R achieved a good diagnostic accuracy for differentiating AD from bvFTD patients and for differentiating AD and bvFTD from the controls in the present sample.

scholarly journals Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI

2016 ◽  
Vol 27 (8) ◽  
pp. 3372-3382 ◽  
Author(s):  
Esther E. Bron ◽  
Marion Smits ◽  
Janne M. Papma ◽  
Rebecca M. E. Steketee ◽  
Rozanna Meijboom ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differential Diagnosis ◽  
Frontotemporal Dementia ◽  
Computer Aided

scholarly journals Subcortical atrophy in frontotemporal dementia and Alzheimer's disease: Significance for differential diagnosis and correlation with clinical manifestations

2008 ◽  
Vol 2 (4) ◽  
pp. 284-288 ◽  
Author(s):  
Renata Teles Vieira ◽  
Leonardo Caixeta
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differential Diagnosis ◽  
Frontotemporal Dementia ◽  
Daily Living ◽  
Independent Living ◽  
Clinical Manifestations ◽  
Linear Measurement ◽  
Clinical Dementia Rating ◽  
Significant Difference

Abstract Cerebral subcortical atrophy occurs in both Alzheimer's disease (AD) and frontotemporal dementia (FTD) but its significance for clinical manifestations and differential diagnosis between these common types of dementia has not been extensively investigated. Objectives: To compare the severity of cerebral subcortical atrophy in FTD and AD and to analyze the correlations between cerebral subcortical atrophy and demographics and clinical characteristics. Methods: Twenty three patients with FTD and 21 with AD formed the sample, which comprised 22 men and 22 women, aged 33 to 89, with mean age (±SD) of 68.52±12.08 years, with schooling ranging from 1 to 20 years, with a mean (±SD) of 7.35±5.54 years, and disease duration with a mean (±SD) of 3.66±3.44 years. The degree of cerebral subcortical atrophy was measured indirectly with a linear measurement of subcortical atrophy, the Bifrontal Index (BFI), using magnetic resonance imaging. We evaluated cognition, activities of daily living and dementia severity with the Mini-Mental State Examination, Functional Activities Questionnaire and the Clinical Dementia Rating, respectively. Results: There was no significant difference (p>0.05) in BFI between FTD and AD. The severity of cognitive deficits (for both FTD and AD groups) and level of daily living activities (only for AD group) were correlated with BFI. Conclusions: A linear measurement of cerebral subcortical atrophy did not differentiate AD from FTD in this sample. Cognitive function (in both FTD and AD groups) and capacity for independent living (only in AD group) were inversely correlated with cerebral subcortical atrophy.

scholarly journals System for Automatic Assessment of Alzheimer’s Disease Diagnosis Based on Deep Learning Techniques

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 28
Author(s):  
Alejandro Puente-Castro ◽  
Cristian Robert Munteanu ◽  
Enrique Fernandez-Blanco
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Reference Data ◽  
Disease Diagnosis ◽  
Resonance Imaging ◽  
Automatic Assessment ◽  
Learning Techniques ◽  
Magnetic Resonance Imaging Mri

Automatic detection of Alzheimer’s disease is a very active area of research. This is due to its usefulness in starting the protocol to stop the inevitable progression of this neurodegenerative disease. This paper proposes a system for the detection of the disease by means of Deep Learning techniques in magnetic resonance imaging (MRI). As a solution, a model of neuronal networks (ANN) and two sets of reference data for training are proposed. Finally, the goodness of this system is verified within the domain of the application.

scholarly journals Using the Disease State Fingerprint Tool for Differential Diagnosis of Frontotemporal Dementia and Alzheimer's Disease

2016 ◽  
Vol 6 (2) ◽  
pp. 313-329 ◽  
Author(s):  
Miguel Ángel Muñoz-Ruiz ◽  
Anette Hall ◽  
Jussi Mattila ◽  
Juha Koikkalainen ◽  
Sanna-Kaisa Herukka ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differential Diagnosis ◽  
Frontotemporal Dementia ◽  
Clinical Decision Making ◽  
Apoe Genotype ◽  
Disease State ◽  
Diagnostic Process ◽  
Computer Assisted ◽  
Study Cohort

Background: Disease State Index (DSI) and its visualization, Disease State Fingerprint (DSF), form a computer-assisted clinical decision making tool that combines patient data and compares them with cases with known outcomes. Aims: To investigate the ability of the DSI to diagnose frontotemporal dementia (FTD) and Alzheimer's disease (AD). Methods: The study cohort consisted of 38 patients with FTD, 57 with AD and 22 controls. Autopsy verification of FTD with TDP-43 positive pathology was available for 14 and AD pathology for 12 cases. We utilized data from neuropsychological tests, volumetric magnetic resonance imaging, single-photon emission tomography, cerebrospinal fluid biomarkers and the APOE genotype. The DSI classification results were calculated with a combination of leave-one-out cross-validation and bootstrapping. A DSF visualization of a FTD patient is presented as an example. Results: The DSI distinguishes controls from FTD (area under the receiver-operator curve, AUC = 0.99) and AD (AUC = 1.00) very well and achieves a good differential diagnosis between AD and FTD (AUC = 0.89). In subsamples of autopsy-confirmed cases (AUC = 0.97) and clinically diagnosed cases (AUC = 0.94), differential diagnosis of AD and FTD performs very well. Conclusions: DSI is a promising computer-assisted biomarker approach for aiding in the diagnostic process of dementing diseases. Here, DSI separates controls from dementia and differentiates between AD and FTD.

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