scholarly journals Deep Learning and Risk Score Classification of Mild Cognitive Impairment and Alzheimer’s Disease

2021 ◽  
Vol 80 (3) ◽  
pp. 1079-1090
Author(s):  
Sanjay Nagaraj ◽  
Tim Q. Duong
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Learning Algorithm ◽  
Selection Methods ◽  
Deep Learning Algorithm ◽  
Neurocognitive Tests ◽  
Risk Stratification Score

Background: Many neurocognitive and neuropsychological tests are used to classify early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer’s disease (AD) from cognitive normal (CN). This can make it challenging for clinicians to make efficient and objective clinical diagnoses. It is possible to reduce the number of variables needed to make a reasonably accurate classification using machine learning. Objective: The goal of this study was to develop a deep learning algorithm to identify a few significant neurocognitive tests that can accurately classify these four groups. We also derived a simplified risk-stratification score model for diagnosis. Methods: Over 100 variables that included neuropsychological/neurocognitive tests, demographics, genetic factors, and blood biomarkers were collected from 383 EMCI, 644 LMCI, 394 AD patients, and 516 cognitive normal from the Alzheimer’s Disease Neuroimaging Initiative database. A neural network algorithm was trained on data split 90% for training and 10% testing using 10-fold cross-validation. Prediction performance used area under the curve (AUC) of the receiver operating characteristic analysis. We also evaluated five different feature selection methods. Results: The five feature selection methods consistently yielded the top classifiers to be the Clinical Dementia Rating Scale - Sum of Boxes, Delayed total recall, Modified Preclinical Alzheimer Cognitive Composite with Trails test, Modified Preclinical Alzheimer Cognitive Composite with Digit test, and Mini-Mental State Examination. The best classification model yielded an AUC of 0.984, and the simplified risk-stratification score yielded an AUC of 0.963 on the test dataset. Conclusion: The deep-learning algorithm and simplified risk score accurately classifies EMCI, LMCI, AD and CN patients using a few common neurocognitive tests.

scholarly journals Risk Score Stratification of Alzheimer’s Disease and Mild Cognitive Impairment using Deep Learning

2020 ◽  
Author(s):  
Sanjay Nagaraj ◽  
Tim Q Duong
Keyword(s):  
Alzheimer’S Disease ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Risk Stratification ◽  
Risk Score ◽  
Learning Algorithm ◽  
Clinical Variables ◽  
Deep Learning Algorithm ◽  
Neurocognitive Tests

ABSTRACTAlzheimer Disease (AD) is a progressive neurodegenerative disease that can significantly impair cognition and memory. AD is the leading cause of dementia and affects one in ten people age 65 and older. Current diagnoses methods of AD heavily rely on the use of Magnetic Resonance Imaging (MRI) since non-imaging methods can vary widely leading to inaccurate diagnoses. Furthermore, recent research has revealed a substage of AD, Mild Cognitive Impairment (MCI), that is characterized by symptoms between normal cognition and dementia which makes it more prone to misdiagnosis.A large battery of clinical variables are currently used to detect cognitive impairment and classify early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD from cognitive normal (CN) patients. The goal of this study was to derive a simplified risk-stratification algorithm for diagnosis and identify a few significant clinical variables that can accurately classify these four groups using an empirical deep learning approach. Over 100 variables that included neuropsychological/neurocognitive tests, demographics, genetic factors, and blood biomarkers were collected from EMCI, LMCI, AD, and CN patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Feature engineering was performed with 5 different methods and a neural network was trained on 90% of the data and tested on 10% using 10-fold cross validation. Prediction performance used area under the curve (AUC) of the receiver operating characteristic analysis.The five different feature selection methods consistently yielded the top classifiers to be the Clinical Dementia Rating Scale - Sum of Boxes (CDRSB), Delayed total recall (LDELTOTAL), Modified Preclinical Alzheimer Cognitive Composite with Trails test (mPACCtrailsB), the Modified Preclinical Alzheimer Cognitive Composite with Digit test (mPACCdigit), and Mini-Mental State Examination (MMSE). The best classification model yielded an AUC of 0.984, and the simplified risk-stratification score yielded an AUC of 0.963 on the test dataset.Our results show that this deep-learning algorithm and simplified risk score derived from our deep-learning algorithm accurately diagnose EMCI, LMCI, AD and CN patients using a few commonly available neurocognitive tests. The project was successful in creating an accurate, clinically translatable risk-stratified scoring aid for primary care providers to diagnose AD in a fast and inexpensive manner.

scholarly journals Automatic Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment Based on CNN + SVM Networks with End-to-End Training

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhe Huang ◽  
Minglang Sun ◽  
Chengan Guo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Onset Time ◽  
Decision Fusion ◽  
Support Vector ◽  
End To End

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease, and, at present, once it has been diagnosed, there is no effective curative treatment. Accurate and early diagnosis of Alzheimer’s disease is crucial for improving the condition of patients since effective preventive measures can be taken in advance to delay the onset time of the disease. 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET : PET) is an effective biomarker of the symptom of AD and has been used as medical imaging data for diagnosing AD. Mild cognitive impairment (MCI) is regarded as an early symptom of AD, and it has been shown that MCI also has a certain biomedical correlation with PET. In this paper, we explore how to use 3D PET images to realize the effective recognition of MCI and thus achieve the early prediction of AD. This problem is then taken as the classification of three categories of PET images, including MCI, AD, and NC (normal controls). In order to get better classification performance, a novel network model is proposed in the paper based on 3D convolution neural networks (CNN) and support vector machines (SVM) by utilizing both the excellent abilities of CNN in feature extraction and SVM in classification. In order to make full use of the optimal property of SVM in solving binary classification problems, the three-category classification problem is divided into three binary classifications, and each binary classification is being realized with a CNN + SVM network. Then, the outputs of the three CNN + SVM networks are fused into a final three-category classification result. An end-to-end learning algorithm is developed to train the CNN + SVM networks, and a decision fusion algorithm is exploited to realize the fusion of the outputs of three CNN + SVM networks. Experimental results obtained in the work with comparative analyses confirm the effectiveness of the proposed method.

scholarly journals A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease

NeuroImage ◽  
2019 ◽  
Vol 189 ◽  
pp. 276-287 ◽  
Author(s):  
Simeon Spasov ◽  
Luca Passamonti ◽  
Andrea Duggento ◽  
Pietro Liò ◽  
Nicola Toschi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Learning Approach

Diagnosis of Alzheimer's disease based on Deeply-Fused Nets

2020 ◽  
Vol 23 ◽  
Author(s):  
ChangZu Chen ◽  
Qi Wu ◽  
ZuoYong Li ◽  
Lei Xiao ◽  
Zhong Yi Hu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Disease Diagnosis ◽  
Automatic Diagnosis ◽  
Single Base ◽  
Base Network ◽  
Normal Controls

Aim and Objective: Fast and accurate diagnosis of Alzheimer's disease is very important for the care and further treatment of patients. Along with the development of deep learning, impressive progress has also been made in the automatic diagnosis of AD. Most existing studies on automatic diagnosis are concerned with a single base network, whose accuracy for disease diagnosis still needs to be improved. This study was undertaken to propose a method to improve the accuracy of automatic diagnosis of AD. Materials and Methods: MRI image data from the Alzheimer’s Disease Neuroimaging Initiative were used to train a deep learning model to achieve computer-aided diagnosis of Alzheimer's disease. The data consisted of 138 with AD, 280 with mild cognitive impairment, and 138 normal controls. Here, a new deeply-fused net is proposed, which combines several deep convolutional neural networks, thereby avoiding the error of a single base network and increasing the classification accuracy and generalization capacity. Results: Experiments show that when differentiating between patients with AD, mild cognitive impairment, and normal controls on a subset of the ADNI database without data leakage, the new architecture improves the accuracy by about 4 percentage points as compared to a single standard base network. Conclusion: This new approach exhibits better performance, but there is still much to be done before its clinical application. In the future, greater research effort will be devoted to improving the performance of the deeply-fused net.

scholarly journals Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer’s Disease or Mild Cognitive Impairment

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lanlan Li ◽  
Yeying Yang ◽  
Qi Zhang ◽  
Jiao Wang ◽  
Jiehui Jiang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Association Studies ◽  
Genomic Research ◽  
Genome Wide Association Studies ◽  
Genetic Biomarkers ◽  
Gwas Analysis

Objectives. Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Certain genes have been identified as important clinical risk factors for AD, and technological advances in genomic research, such as genome-wide association studies (GWAS), allow for analysis of polymorphisms and have been widely applied to studies of AD. However, shortcomings of GWAS include sensitivity to sample size and hereditary deletions, which result in low classification and predictive accuracy. Therefore, this paper proposes a novel deep-learning genomics approach and applies it to multitasking classification of AD progression, with the goal of identifying novel genetic biomarkers overlooked by traditional GWAS analysis. Methods. In this study, we selected genotype data from 1461 subjects enrolled in the Alzheimer’s Disease Neuroimaging Initiative, including 622 AD, 473 mild cognitive impairment (MCI), and 366 healthy control (HC) subjects. The proposed deep-learning genomics (DLG) approach consists of three steps: quality control, coding of single-nucleotide polymorphisms, and classification. The ResNet framework was used for the DLG model, and the results were compared with classifications by simple convolutional neural network structure. All data were randomly assigned to one training/validation group and one test group at a ratio of 9 : 1. And fivefold cross-validation was used. Results. We compared classification results from the DLG model to those from traditional GWAS analysis among the three groups. For the AD and HC groups, the accuracy, sensitivity, and specificity of classification were, respectively, 98.78 ± 1.50 % , 98.39 % ± 2.50 % , and 99.44 % ± 1.11 % using the DLG model, while 71.38 % ± 0.63 % , 63.13 % ± 2.87 % , and 85.59 % ± 6.66 % using traditional GWAS. Similar results were obtained from the other two intergroup classifications. Conclusion. The DLG model can achieve higher accuracy and sensitivity when applied to progression of AD. More importantly, we discovered several novel genetic biomarkers of AD progression, including rs6311 and rs6313 in HTR2A, rs1354269 in NAV2, and rs690705 in RFC3. The roles of these novel loci in AD should be explored in future research.

scholarly journals A Novel Ensemble-Based Machine Learning Algorithm To Predict The Conversion From Mild Cognitive Impairment To Alzheimer’s Disease Using Socio-demographic Characteristics, Clinical Information And Neuropsychological Measures

2019 ◽  
Author(s):  
Massimiliano Grassi ◽  
Nadine Rouleaux ◽  
Daniela Caldirola ◽  
David Loewenstein ◽  
Koen Schruers ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Disease Stage ◽  
Machine Learning Algorithm

ABSTRACTBackgroundDespite the increasing availability in brain health related data, clinically translatable methods to predict the conversion from Mild Cognitive Impairment (MCI) to Alzheimer’s disease (AD) are still lacking. Although MCI typically precedes AD, only a fraction of 20-40% of MCI individuals will progress to dementia within 3 years following the initial diagnosis. As currently available and emerging therapies likely have the greatest impact when provided at the earliest disease stage, the prompt identification of subjects at high risk for conversion to full AD is of great importance in the fight against this disease. In this work, we propose a highly predictive machine learning algorithm, based only on non-invasively and easily in-the-clinic collectable predictors, to identify MCI subjects at risk for conversion to full AD.MethodsThe algorithm was developed using the open dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), employing a sample of 550 MCI subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. A restricted set of information regarding sociodemographic and clinical characteristics, neuropsychological test scores was used as predictors and several different supervised machine learning algorithms were developed and ensembled in final algorithm. A site-independent stratified train/test split protocol was used to provide an estimate of the generalized performance of the algorithm.ResultsThe final algorithm demonstrated an AUROC of 0.88, sensitivity of 77.7%, and a specificity of 79.9% on excluded test data. The specificity of the algorithm was 40.2% for 100% sensitivity.DiscussionThe algorithm we developed achieved sound and high prognostic performance to predict AD conversion using easily clinically derived information that makes the algorithm easy to be translated into practice. This indicates beneficial application to improve recruitment in clinical trials and to more selectively prescribe new and newly emerging early interventions to high AD risk patients.

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