Alzheimer's Disease Classification Using Population-Referenced Brain Volumetric Percentiles.

Background: Machine learning is now a powerful tool to help improve medical disorders diagnosis. One of its critical applications is the classification or clustering of neurodegenerative disease by pattern recognition methods based on biomedical signals and medical images. Early detection of these diseases is always useful and vital. In this study, we focused on Alzheimer’s disease (AD) as a type of dementia leading to problems with memory, thinking, and behavior. This disease was named after Dr. Alois Alzheimer in 1906 when he inspected a female patient who died of an unusual mental illness. According to recent studies, four stages are introduced for AD, including pre-dementia, early AD, moderate AD, and advanced AD. There are several methods for AD diagnosis that include mental status evaluation, physical exam, and neurological exam, based on different imaging techniques such as magnetic resonance imaging (MRI). Several methods have been introduced until now for the classification and detection of AD using machine learning algorithms, such as the classification of AD with discrete wavelet transform (DWT) and single linear discriminant analysis (LDA) classifiers and differentiation of AD from normal based on T2-weighted MRI with shearlet transform (ST) and K-nearest neighbors (KNN) classifiers. Objectives: In this work, we proposed a methodology based on DWT with three-level decomposition feature extraction (Figure 1). Methods: Based on statistics (mean, variance, skewness) of features and principal component analysis (PCA) for dimension reduction, we used five classifiers, including multi-layer perceptron neural network (MLPNN) (Figure 2), KNN, support vector machine (SVM) (Figure 3), and naive Bayesian (NB) with the majority vote method to fuse them into one ensemble classifier. Results: The proposed methodology was evaluated using 100 T2-weighted MRI of AD and cognitive normal (CN) subjects, which were chosen from the Harvard Medical School website. The accuracy, specificity, and sensitivity achieved from our methodology were 95%, 90%, and 100%, respectively by using a 10-fold cross-validation strategy. Conclusion: Our study showed that the stacking method for classification of AD and CN was better than using one classifier and comparable with state-of-the-art methods.

ABSTRACTMultimodal neuroimaging, combining data from different sources, has shown promise in the classification of the Alzheimer's disease (AD) stage. Existing multimodal neuroimaging fusion methods exhibit certain limitations, which require advancements to enhance their objective performance, sensitivity, and specificity for AD classification. This study uses the use of a Pareto‐optimal cosine color map to enhance classification performance and visual clarity of fused images. A mobile vision transformer (ViT) model, incorporating the swish activation function, is introduced for effective feature extraction and classification. Fused images from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Whole Brain Atlas (AANLIB), and Open Access Series of Imaging Studies (OASIS) datasets, obtained through optimized transposed convolution, are utilized for model training, while evaluation is achieved using images that have not been fused from the same databases. The proposed model demonstrates high accuracy in AD classification across different datasets, achieving 98.76% accuracy for Early Mild Cognitive Impairment (EMCI) versus LMCI, 98.65% for Late Mild Cognitive Impairment (LMCI) versus AD, 98.60% for EMCI versus AD, and 99.25% for AD versus Cognitive Normal (CN) in the ADNI dataset. Similarly, on OASIS and AANLIB, the precision of the AD versus CN classification is 99.50% and 96.00%, respectively. Evaluation metrics showcase the model's precision, recall, and F1 score for various binary classifications, emphasizing its robust performance.

Reducing variations in multi-center Alzheimer's disease classification with convolutional adversarial autoencoder.

BackgroundRecent advances in deep learning have allowed multi‐omics data to be used to detect early stages of Alzheimer's disease (AD). The metabolome, as the end‐product of biological cascades including the genome, transcriptome, and proteome, may have informative elements that could serve as potential AD biomarkers. We report a novel two‐step deep learning method to detect AD and progression using lipidomics data.MethodWe used serum‐based cross‐sectional lipidome data with 781 lipids from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) including 216 cognitively normal (CN), 635 MCI, and 382 dementia (AD). Phenotype influence scores (PIS) was derived by deep learning‐based circling Sliding Window Association Test approach (Circling SWAT) (Fig. 1‐A), an extension of SWAT (Jo et al., 2022) with correlation heatmap and dendrogram analysis for omics data with minimal features. We used convolutional neural networks (CNN) to classify AD from CN based on top‐scoring metabolites. The AD classification model was used to predict conversion of MCI to AD within two years using 146 MCI‐convertors who developed AD and 190 MCI‐Stable who did not develop AD over two years from baseline. We removed two deDE lipids, deDE(18:2) and deDE(20:4), because their associations with AD were driven mainly by AD‐related medication.ResultPhenotype influence scores (PIS) were calculated for each lipid using circling SWAT to determine their impacts on AD. The metabolites with the highest PIS were LPE(22:6), PC(39:5), and PC(15‐MHDA_22:6). When CNN was applied to the top 50 lipids by PIS, AD and CN classification accuracy was 73.6% (Fig. 1‐B), which was 5.1% higher than the accuracy obtained using only age, sex, and APOE ε4. For MCI conversion to AD, the AD/CN classifier accuracy was 69.9% or 8.8% higher than a model using only age, sex, and APOE ε4.ConclusionThe circling SWAT method appears promising for the analysis of lipidome data as it was able to identify several AD‐related lipids and predict disease progression.

Purpose: Hard-to-interpret Black-box Machine Learning (ML) were often used for early Alzheimer's Disease (AD) detection. Methods: To interpret eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM) black-box models a workflow based on Shapley values was developed. All models were trained on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and evaluated for an independent ADNI test set, as well as the external Australian Imaging and Lifestyle flagship study of Ageing (AIBL), and Open Access Series of Imaging Studies (OASIS) datasets. Shapley values were compared to intuitively interpretable Decision Trees (DTs), and Logistic Regression (LR), as well as natural and permutation feature importances. To avoid the reduction of the explanation validity caused by correlated features, forward selection and aspect consolidation were implemented. Results: Some black-box models outperformed DTs and LR. The forward-selected features correspond to brain areas previously associated with AD. Shapley values identified biologically plausible associations with moderate to strong correlations with feature importances. The most important RF features to predict AD conversion were the volume of the amygdalae, and a cognitive test score. Good cognitive test performances and large brain volumes decreased the AD risk. The models trained using cognitive test scores significantly outperformed brain volumetric models ($p<0.05$). Cognitive Normal (CN) vs. AD models were successfully transferred to external datasets. Conclusion: In comparison to previous work, improved performances for ADNI and AIBL were achieved for CN vs. Mild Cognitive Impairment (MCI) classification using brain volumes. The Shapley values and the feature importances showed moderate to strong correlations.

The disease popularly known as Alzheimer’s disease (AD), which causes cognitive impairment, is extensively distributed throughout the world. The lack of a cure or strategy to stop the growth of the disease notwithstanding, it is one of the most extensively studied illnesses in medicine and healthcare. However, patients with AD have a variety of treatment options to select from, including pharmaceuticals or non-drug alternatives, to help them maintain their quality of life. Over time, patients will need to be treated differently based on how far into the disease process they are. In this regard, early detection and classification of the illness stages can have a substantial impact on the treatment of symptoms. Early detection of Alzheimer’s disease is critical for the analysis of patient medications and the selection of appropriate specialists. The goal of this project is to provide a comprehensive comparison of the techniques used in the detection and classification of Alzheimer’s disease. In medical research, machine learning algorithms can be used to uncover facts, particularly in disease prediction. Support vector machines (SVM), decision trees, Bayes classifiers, K-nearest neighbors (KNN), gradient boosting, and other machine learning algorithms are used to diagnose a variety of diseases and conditions. The application of machine learning algorithms can result in the quick and highly accurate prediction and classification of this disease. This project helps in analyses of different machine learning techniques and algorithms that are used to predict and classify and provides an extensive accuracy comparison of the machine learning techniques used for the prediction and classification of Alzheimer’s disease.

To develop and validate an automatic classification algorithm for diagnosing Alzheimer's disease (AD) or mild cognitive impairment (MCI). This study evaluated a high-performance interpretable network algorithm (TabNet) and compared its performance with that of XGBoost, a widely used classifier. Brain segmentation was performed using a commercially approved software. TabNet and XGBoost were trained on the volumes or radiomics features of 102 segmented regions for classifying subjects into AD, MCI, or cognitively normal (CN) groups. The diagnostic performances of the two algorithms were compared using areas under the curves (AUCs). Additionally, 20 deep learning-based AD signature areas were investigated. Between December 2014 and March 2017, 161 AD, 153 MCI, and 306 CN cases were enrolled. Another 120 AD, 90 MCI, and 141 CN cases were included for the internal validation. Public datasets were used for external validation. TabNet with volume features had an AUC of 0.951 (95% confidence interval [CI], 0.947-0.955) for AD vs CN, which was similar to that of XGBoost (0.953 [95% CI, 0.951-0.955], p = 0.41). External validation revealed the similar performances of two classifiers using volume features (0.871 vs. 0.871, p = 0.86). Likewise, two algorithms showed similar performances with one another in classifying MCI. The addition of radiomics data did not improve the performance of TabNet. TabNet and XGBoost focused on the same 13/20 regions of interest, including the hippocampus, inferior lateral ventricle, and entorhinal cortex. TabNet shows high performance in AD classification and detailed interpretation of the selected regions. Using a high-performance interpretable deep learning network, the automatic classification algorithm assisted in accurate Alzheimer's disease detection using 3D T1-weighted brain MRI and detailed interpretation of the selected regions. • MR volumetry data revealed that TabNet had a high diagnostic performance in differentiating Alzheimer's disease (AD) from cognitive normal cases, which was comparable with that of XGBoost. • The addition of radiomics data to the volume data did not improve the diagnostic performance of TabNet. • Both TabNet and XGBoost selected the clinically meaningful regions of interest in AD, including the hippocampus, inferior lateral ventricle, and entorhinal cortex.

Graph-based representations are becoming more common in the medical domain, where each node defines a patient, and the edges signify associations between patients, relating individuals with disease and symptoms in a node classification task. In this study, a Graph Convolutional Networks (GCN) model was utilized to capture differences in neurocognitive, genetic, and brain atrophy patterns that can predict cognitive status, ranging from Normal Cognition (NC) to Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD), on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Elucidating model predictions is vital in medical applications to promote clinical adoption and establish physician trust. Therefore, we introduce a decomposition-based explanation method for individual patient classification. Our method involves analyzing the output variations resulting from decomposing input values, which allows us to determine the degree of impact on the prediction. Through this process, we gain insight into how each feature from various modalities, both at the individual and group levels, contributes to the diagnostic result. Given that graph data contains critical information in edges, we studied relational data by silencing all the edges of a particular class, thereby obtaining explanations at the neighborhood level. Our functional evaluation showed that the explanations remain stable with minor changes in input values, specifically for edge weights exceeding 0.80. Additionally, our comparative analysis against SHAP values yielded comparable results with significantly reduced computational time. To further validate the model's explanations, we conducted a survey study with 11 domain experts. The majority (71%) of the responses confirmed the correctness of the explanations, with a rating of above six on a 10-point scale for the understandability of the explanations. Strategies to overcome perceived limitations, such as the GCN's overreliance on demographic information, were discussed to facilitate future adoption into clinical practice and gain clinicians' trust as a diagnostic decision support system.

Alzheimer's disease is an incurable neurodegenerative disease that affects brain memory mainly in aged people. Alzheimer's disease occurs worldwide and mainly affects people aged older than 65 years. Early diagnosis for accurate detection is needed for this disease. Manual diagnosis by health specialists is error prone and time consuming due to the large number of patients presenting with the disease. Various techniques have been applied to the diagnosis and classification of Alzheimer's disease but there is a need for more accuracy in early diagnosis solutions. The model proposed in this research suggests a deep learning-based solution using DenseNet-169 and ResNet-50 CNN architectures for the diagnosis and classification of Alzheimer's disease. The proposed model classifies Alzheimer's disease into Non-Dementia, Very Mild Dementia, Mild Dementia, and Moderate Dementia. The DenseNet-169 architecture outperformed in the training and testing phases. The training and testing accuracy values for DenseNet-169 are 0.977 and 0.8382, while the accuracy values for ResNet-50 were 0.8870 and 0.8192. The proposed model is usable for real-time analysis and classification of Alzheimer's disease.

Alzheimer's disease (AD) is a progressive illness that can cause behavioural abnormalities, personality changes, and memory loss. Early detection helps with future planning for both the affected person and caregivers. Thus, an innovative hybrid Deep Learning (DL) method is introduced for the segmentation and classification of AD. The classification is performed by a Fuzzy Res-LeNet model. At first, an input Magnetic Resonance Imaging (MRI) image is attained from the database. Image preprocessing is then performed by a Bilateral Filter (BF) to enhance the quality of image by denoising. Then segmentation is carried out by the proposed O-SegUNet. This method integrates the O-SegNet and U-Net model using Pearson correlation coefficient-based fusion. After the segmentation, augmentation is carried out by utilizing Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. After that, feature extraction is carried out. Finally, AD classification is performed by the Fuzzy Res-LeNet. The stages are classified as Mild Cognitive Impairment (MCI), AD, Cognitive Normal (CN), Early Mild Cognitive Impairment (EMCI), and Late Mild Cognitive Impairment(LMCI). Here, Fuzzy Res-LeNet is devised by integrating Fuzzy logic, ResNeXt, and LeNet. Furthermore, the proposed Fuzzy Res-LeNet obtained the maximum performance with an accuracy of 93.887%, sensitivity of 94.587%, and specificity of 94.008%.

Functional magnetic resonance imaging (fMRI) is widely used to capture brain functional activities, but its high-dimensional nature and temporal complexity pose significant challenges for feature representation. To address these issues, this study proposes an end-to-end deep learning network for fMRI feature encoding and validates its effectiveness through Alzheimer's disease (AD) classification task. The proposed network first employs a 3D CNN to perform spatial encoding of fMRI at each time point, extracting local spatial patterns of brain images. Then, a transformer attention block specifically designed for 3D MRI is designed, which enhances spatial feature modeling through 3D positional encoding and captures long-range dependencies between brain regions. Finally, a cascaded transformer module is constructed to integrate spatial features across different time points, modeling the dynamic changes in brain activity. Experimental results on two fMRI datasets from ADNI database demonstrate that the proposed method improves feature representation and AD classification performance. By effectively capturing both the spatial and temporal characteristics of fMRI data, this approach provides a robust solution for automated fMRI feature encoding. The code is available at https://github.com/Yanteng32/CTF_FMRI.

Alzheimer's disease damages brain neurons, resulting in memory loss. Early and accurate diagnosis of the disease is crucial for implementing preventive measures. However, differentiating between Alzheimer’s and healthy data in older adults is challenging due to the similarities in their brain patterns and intensities, complicating researchers' efforts to make an accurate diagnosis. Therefore, the research aims to use machine learning to improve diagnosis and classification of the disease, such as support vector machines (SVM), decision trees, and feedforward neural networks (FFNN). Classification algorithms were applied to the Alzheimer’s disease dataset, including 2149 cases, and the models were evaluated through metrics (Accuracy, Precision, Recall, specificity, F1 Score, F2 Score, F3 score, and AUC). Following data analysis and obtaining the results, we reached the Decision Tree model excels across all metrics, achieving high scores in accuracy (96.32%), precision (94.63%), recall (95%), specificity (97.05%), and AUC (94.96%). This demonstrates its ability to correctly identify true positives and negatives, and reduce false positives and negatives, makes it the most reliable model for accurately classifying Alzheimer's disease cases. In contrast, the SVM linear and FFANN models offer a good balance with accuracy (83.53% and 83.57%), specificity (89.27% and 91.72%), and AUC (89.63% and 89.84%). However, their lower recall (73.03% and 68.68%) compared to the Decision Tree may result in missed positive cases, making them less effective for classification. The SVM RBF model is the least effective option, with high precision and specificity but poor performance across all other metrics and lacks overall balance, resulting in a high number of false negatives. metrics and lacks overall balance, resulting in a high number of false negatives. Conclusion: The decision tree model outperforms other models, making it the best choice for Alzheimer's disease classification.

Alzheimer's Disease (AD) classification is very helpful for timely treatment and intervention in its early stage so as to reduce the incidence rate of AD and delay AD progression in elderly population. In this paper, a feature extraction method utilizing Euclidean distance map was proposed. Then the features for AD classification were selected in accordance with the feature importance score calculated by LightGBM (LGB). At last, the LGB classifier was applied to classifying AD patient utilizing selected features. The proposed AD classification method was evaluated on real dataset. The experimental results show that the proposed feature extraction and selection methods are effective, and the LGB classifier using selected features is superior to other conventional classifiers.

OPINION article Front. Aging Neurosci., 26 August 2015Sec. Alzheimer's Disease and Related Dementias Volume 7 - 2015 | https://doi.org/10.3389/fnagi.2015.00164

ObjectiveTo evaluate the effect of the apolipoprotein E (APOE) ϵ4 in the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) in ethnic Chinese people in Taiwan.MethodsSubjects older than...