Dementia is a neuropsychiatric brain disorder that usually occurs when one or more brain cells stop working partially or at all. Diagnosis of this disorder in the early phases of the disease is a vital task to rescue patients lives from bad consequences and provide them with better healthcare. Machine learning methods have been proven to be accurate in predicting dementia in the early phases of the disease. The prediction of dementia depends heavily on the type of collected data which usually are gathered from Normalized Whole Brain Volume (nWBV) and Atlas Scaling Factor (ASF) which are normally measured and corrected from Magnetic Resonance Imaging (MRIs). Other biological features such as age and gender can also help in the diagnosis of dementia. Although many studies use machine learning for predicting dementia, we could not reach a conclusion on the stability of these methods for which one is more accurate under different experimental conditions. Therefore, this paper investigates the conclusion stability regarding the performance of machine learning algorithms for dementia prediction. To accomplish this, a large number of experiments were run using 7 machine learning algorithms and two feature reduction algorithms namely, Information Gain (IG) and Principal Component Analysis (PCA). To examine the stability of these algorithms, thresholds of feature selection were changed for the IG from 20% to 100% and the PCA dimension from 2 to 8. This has resulted in 7x9 + 7x7= 112 experiments. In each experiment, various classification evaluation data were recorded. The obtained results show that among seven algorithms the support vector machine and Naive Bayes are the most stable algorithms while changing the selection threshold. Also, it was found that using IG would seem more efficient than using PCA for predicting Dementia.
Simon Fraser University（西蒙弗雷泽大学）；Wake Forest University（维克森林大学）；University of British Columbia（不列颠哥伦比亚大学）；Ohio State University Wexner Medical Center（俄亥俄州立大学韦克斯纳医学中心）；University of Miami Miller School of Medicine（迈阿密大学米勒医学院）；University of Nottingham（诺丁汉大学）
Background: The increasing availability of databases containing both magnetic resonance imaging (MRI) and genetic data allows researchers to utilize multimodal data to better understand the characteristics of dementia of Alzheimer's type (DAT). Objective: The goal of this study was to develop and analyze novel biomarkers that can help predict the development and progression of DAT. Methods: We used feature selection and ensemble learning classifier to develop an image/genotype-based DAT score that represents a subject's likelihood of developing DAT in the future. Three feature types were used:…
Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning
Department of Computer Science and Engineering, R.V. College of Engineering（计算机科学与工程系，R.V.工程学院）；School of Computer Science, University of Nottingham（计算机科学学院，诺丁汉大学）
REVEAL: Multimodal Vision-Language Alignment of Retinal Morphometry and Clinical Risks for Incident AD and Dementia Prediction
J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida（佛罗里达大学J. Crayton Pruitt家族生物医学工程系）；Research Institute of Electrical Communication, Tohoku University（东北大学电气通信研究所）；Department of Applied Mathematics & Statistics, Stony Brook University（石溪大学应用数学与统计学系）；Department of Computer Science, Stony Brook University（石溪大学计算机科学系）
BrainNet-MoE: Brain-Inspired Mixture-of-Experts Learning for Neurological Disease Identification
The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Indiana University Indianapolis（印第安纳大学印第安纳波利斯分校）；University of Cambridge（剑桥大学）；University of Sheffield（谢菲尔德大学）；The University of Georgia（佐治亚大学）
Sharif University of Technology（谢里夫理工大学）；Iranian Brain Mapping Biobank（伊朗脑图谱生物库）；National Brain Mapping Laboratory（国家脑图谱实验室）；Virtual University of Medical Sciences（虚拟医科大学）
Early detection of dementia through specific biomarkers in MR images plays a critical role in developing support strategies proactively. Fazekas scale facilitates an accurate quantitative assessment of the severity of white matter lesions and hence the disease. Imaging Biomarkers of dementia are multiple and comprehensive documentation of them is time-consuming. Therefore, any effort to automatically extract these biomarkers will be of clinical value while reducing inter-rater discrepancies. To tackle this problem, we propose to classify the disease severity based on the Fazekas scale through …
NeuroBridge: Bridging Multi-Task MRI Knowledge for Neurodegenerative Disease Diagnosis
Department of Mechanical Engineering, Boston University（波士顿大学机械工程系）；The Photonics Center, Boston University（波士顿大学光子中心）；Rafik B. Hariri Institute for Computing and Computational Science & Engineering, Boston University（波士顿大学拉菲克·B·哈里里计算与计算科学与工程研究所）；Department of Radiology, Boston University Chobanian & Avedisian School of Medicine（波士顿大学切博尼亚与阿维迪亚安医学学院放射科）；Department of Radiology, Boston Medical Center（波士顿医学中心放射科）；Department of Electrical and Computer Engineering, Boston University（波士顿大学电气与计算机工程系）；Department of Biomedical Engineering, Boston University（波士顿大学生物医学工程系）；Division of Materials Science and Engineering, Boston University（波士顿大学材料科学与工程系）
Alzheimer's disease (AD) is the most common age-related dementia. Mild cognitive impairment (MCI) is the early stage of cognitive decline before AD. It is crucial to predict the MCI-to-AD conversion for precise management, which remains challenging due to the diversity of patients. Previous evidence shows that the brain network generated from diffusion MRI promises to classify dementia using deep learning. However, the limited availability of diffusion MRI challenges the model training. In this study, we develop a self-supervised contrastive learning approach to generate structural brain netwo…
A Multimodal Framework for Dementia Detection via Linguistic and Acoustic Representation Learning
Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens（决策支持系统实验室，电气与计算机工程学院，国家技术大学雅典）
Exploring Multimodal Approaches for Alzheimer's Disease Detection Using Patient Speech Transcript and Audio Data
South China University of Technology（华南理工大学）；University of Georgia（佐治亚大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Massachusetts General Hospital（麻省总医院）
School of Information Technology（信息技术学院）；University of Cincinnati（辛辛那提大学）
Alzheimer's disease is a degenerative brain disease. Being the primary cause of Dementia in adults and progressively destroys brain memory. Though Alzheimer's disease does not have a cure currently, diagnosing it at an earlier stage will help reduce the severity of the disease. Thus, early diagnosis of Alzheimer's could help to reduce or stop the disease from progressing. In this paper, we proposed a deep convolutional neural network-based model for learning model using to determine the stage of Dementia in adults based on the Magnetic Resonance Imaging (MRI) images to detect the early onset o…
Explainable Graph Neural Networks: Understanding Brain Connectivity and Biomarkers in Dementia
School of Computing Technologies RMIT University Melbourne VIC Australia；Department of Biological Sciences；Department of Computer Science \& Information Systems Birla Institute of Technology；Institute of Innovation, Science；Sustainability Federation University Australia Ballarat VIC Australia；RMIT University；Birla Institute of Technology；Federation University Australia
Deep Learning in Early Alzheimer's disease's Detection: A Comprehensive Survey of Classification, Segmentation, and Feature Extraction Methods
University of Wah（瓦赫大学）；NUST（国家科技大学）；Washington University of Science and Technology Virginia（弗吉尼亚华盛顿科技大学）；Kansas State University（堪萨斯州立大学）；University of Alaska Anchorage（阿拉斯加安克雷奇大学）；State University of New York Polytechnic Institute（纽约州立大学理工学院）
Washington University School of Medicine（华盛顿大学医学院）
Objective Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We aimed to conduct a systematic literature review (SLR) of studies that applied machine learning (ML) methods to clinical data derived from electronic health records in order to model risk for progression of AD dementia. Materials and Methods: We searched for articles published between January 1, 2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital Library, Association for Computing Machinery…
University of Bristol（布里斯托大学）；Cookpad Ltd（Cookpad有限公司）
There is a pressing need to automatically understand the state and progression of chronic neurological diseases such as dementia. The emergence of state-of-the-art sensing platforms offers unprecedented opportunities for indirect and automatic evaluation of disease state through the lens of behavioural monitoring. This paper specifically seeks to characterise behavioural signatures of mild cognitive impairment (MCI) and Alzheimer's disease (AD) in the \textit{early} stages of the disease. We introduce bespoke behavioural models and analyses of key symptoms and deploy these on a novel dataset o…
Alzheimer's disease (AD) is an irreversible brain disease that can dramatically reduce quality of life, most commonly manifesting in older adults and eventually leading to the need for full-time care. Early detection is fundamental to slowing its progression; however, diagnosis can be expensive, time-consuming, and invasive. In this work we develop a neural model based on a CNN-LSTM architecture that learns to detect AD and related dementias using targeted and implicitly-learned features from conversational transcripts. Our approach establishes the new state of the art on the DementiaBank data…
Technical University Munich（慕尼黑工业大学）；Ludwig Maximilians University Munich（慕尼黑大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Alzheimer's disease (AD) is the most common cause of dementia. An early detection is crucial for slowing down the disease and mitigating risks related to the progression. While the combination of MRI and FDG-PET is the best image-based tool for diagnosis, FDG-PET is not always available. The reliable detection of Alzheimer's disease with only MRI could be beneficial, especially in regions where FDG-PET might not be affordable for all patients. To this end, we propose a multi-task method based on U-Net that takes T1-weighted MR images as an input to generate synthetic FDG-PET images and classif…
Variational Mixture of Graph Neural Experts for Alzheimer's Disease Recognition across Frequency Bands in EEG Brain Networks
Department of Industrial and Systems Engineering, Rutgers University（鲁特大学工业与系统工程系）；Department of Psychiatry and Behavioral Sciences, University of Minnesota（明尼苏达大学精神病学与行为科学系）；Institute of Brain Science, College of Medicine, National Yang-Ming Chiao Tung University（国家阳明交通大学医学院脑科学研究所）
Queen Mary University of London（伦敦玛丽女王大学）；Jožef Stefan Institute（约热夫·斯特凡研究所）
This paper is a submission to the Alzheimer's Dementia Recognition through Spontaneous Speech (ADReSS) challenge, which aims to develop methods that can assist in the automated prediction of severity of Alzheimer's Disease from speech data. We focus on acoustic and natural language features for cognitive impairment detection in spontaneous speech in the context of Alzheimer's Disease Diagnosis and the mini-mental state examination (MMSE) score prediction. We proposed a model that obtains unimodal decisions from different LSTMs, one for each modality of text and audio, and then combines them us…
Northwestern University（西北大学）；Northwestern Memorial Hospital（西北纪念医院）
We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures. Deep learning methods for classification tasks that utilize structural neuroimaging often require extensive learning parameters to optimize. Frequently, these approaches for automated medical diagnosis also lack visual interpretability for areas in the brain involved in making a diagnosis. This work: (a) analyzes brain shape using surface information of the cortex and subcortical structures, (b) proposes a residual learning fr…
University College London（伦敦大学学院）；Indiana University（印第安纳大学）；Panakeia Technologies（Panakeia 科技公司）
Biomechanical modeling of tissue deformation can be used to simulate different scenarios of longitudinal brain evolution. In this work,we present a deep learning framework for hyper-elastic strain modelling of brain atrophy, during healthy ageing and in Alzheimer's Disease. The framework directly models the effects of age, disease status, and scan interval to regress regional patterns of atrophy, from which a strain-based model estimates deformations. This model is trained and validated using 3D structural magnetic resonance imaging data from the ADNI cohort. Results show that the framework ca…
Introduction: Alzheimer's disease is a type of dementia in which early diagnosis plays a major rule in the quality of treatment. Among new works in the diagnosis of Alzheimer's disease, there are many of them analyzing the voice stream acoustically, syntactically or both. The mostly used tools to perform these analysis usually include machine learning techniques. Objective: Designing an automatic machine learning based diagnosis system will help in the procedure of early detection. Also, systems, using noninvasive data are preferable. Methods: We used are classification system based on spoken …
Simon Fraser University（西蒙弗雷泽大学）；Wake Forest University（维克森林大学）；University of Victoria（维多利亚大学）；University of Nottingham（诺丁汉大学）；Memorial University of Newfoundland（纽芬兰纪念大学）；University of British Columbia（不列颠哥伦比亚大学）；University of Miami Miller School of Medicine（迈阿密大学米勒医学院）；Ohio State University Wexner Medical Center（俄亥俄州立大学韦克斯纳医学中心）
Dementia of Alzheimer's Type (DAT) is a complex disorder influenced by numerous factors, but it is unclear how each factor contributes to disease progression. An in-depth examination of these factors may yield an accurate estimate of time-to-conversion to DAT for patients at various disease stages. We used 401 subjects with 63 features from MRI, genetic, and CDC (Cognitive tests, Demographic, and CSF) data modalities in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We used a deep learning-based survival analysis model that extends the classic Cox regression model to predict …
University of Cambridge（剑桥大学）；Shanghai General Hospital, Shanghai Jiao Tong University（上海交通大学附属第一人民医院）；University of Bath（巴斯大学）
Alzheimer's disease (AD) is the most common age-related dementia. It remains a challenge to identify the individuals at risk of dementia for precise management. Brain MRI offers a noninvasive biomarker to detect brain aging. Previous evidence shows that the brain structural change detected by diffusion MRI is associated with dementia. Mounting studies has conceptualised the brain as a complex network, which has shown the utility of this approach in characterising various neurological and psychiatric disorders. Therefore, the structural connectivity shows promise in dementia classification. The…
Erasmus MC（伊拉斯谟医学中心）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；University College London（伦敦大学学院）
Machine learning methods exploiting multi-parametric biomarkers, especially based on neuroimaging, have huge potential to improve early diagnosis of dementia and to predict which individuals are at-risk of developing dementia. To benchmark algorithms in the field of machine learning and neuroimaging in dementia and assess their potential for use in clinical practice and clinical trials, seven grand challenges have been organized in the last decade. The seven grand challenges addressed questions related to screening, clinical status estimation, prediction and monitoring in (pre-clinical) dement…
An explainable framework for the relationship between dementia and glucose metabolism patterns
Department of Signal Processing and Biomedical Applications, University of Granada（信号处理与生物医学应用系，格拉纳达大学）；Department of Statistics and Operations Research, University of Valencia（统计与运筹学系，瓦伦西亚大学）；Department of Communication Engineering, University of Malaga（通信工程系，马拉加大学）
An Interpretable Ensemble Framework for Multi-Omics Dementia Biomarker Discovery Under HDLSS Conditions
Department of Mathematics and Physics, Mathematics Major, Gangneung-Wonju National University（数学与物理系，数学专业，江陵-Wonju国立大学）；Department of Data Science, Gangneung-Wonju National University（数据科学系，江陵-Wonju国立大学）
Univ. Bordeaux（波尔多大学）；CNRS（法国国家科学研究中心）；Bordeaux INP（波尔多国立高等物理、数学与信息技术学院）；Universitat Politècnia de València（瓦伦西亚理工大学）
Alzheimer's disease is the most common dementia leading to an irreversible neurodegenerative process. To date, subject revealed advanced brain structural alterations when the diagnosis is established. Therefore, an earlier diagnosis of this dementia is crucial although it is a challenging task. Recently, many studies have proposed biomarkers to perform early detection of Alzheimer's disease. Some of them have proposed methods based on inter-subject similarity while other approaches have investigated framework using intra-subject variability. In this work, we propose a novel framework combining…
Erasmus MC（伊拉斯姆斯医学中心）；Delft University of Technology（代尔夫特理工大学）；University of Copenhagen（哥本哈根大学）
Event-based models (EBM) are a class of disease progression models that can be used to estimate temporal ordering of neuropathological changes from cross-sectional data. Current EBMs only handle scalar biomarkers, such as regional volumes, as inputs. However, regional aggregates are a crude summary of the underlying high-resolution images, potentially limiting the accuracy of EBM. Therefore, we propose a novel method that exploits high-dimensional voxel-wise imaging biomarkers: n-dimensional discriminative EBM (nDEBM). nDEBM is based on an insight that mixture modeling, which is a key element …
RIKEN Center for Advanced Intelligence Project (AIP)（理化学研究所先进智能项目中心（AIP））
Dementia and especially Alzheimer's disease (AD) are the most common causes of cognitive decline in elderly people. A spread of the above mentioned mental health problems in aging societies is causing a significant medical and economic burden in many countries around the world. According to a recent World Health Organization (WHO) report, it is approximated that currently, worldwide, about 47 million people live with a dementia spectrum of neurocognitive disorders. This number is expected to triple by 2050, which calls for possible application of AI-based technologies to support an early scree…
There is a need for automatic diagnosis of certain diseases from medical images that could help medical practitioners for further assessment towards treating the illness. Alzheimers disease is a good example of a disease that is often misdiagnosed. Alzheimers disease (Hear after referred to as AD), is caused by atrophy of certain brain regions and by brain cell death and is the leading cause of dementia and memory loss [1]. MRI scans reveal this information but atrophied regions are different for different individuals which makes the diagnosis a bit more trickier and often gets misdiagnosed [1…
Universidad Carlos III de Madrid（马德里卡洛斯三世大学）；University of Eastern Finland（东芬兰大学）
Machine learning techniques typically applied to dementia forecasting lack in their capabilities to jointly learn several tasks, handle time dependent heterogeneous data and missing values. In this paper, we propose a framework using the recently presented SSHIBA model for jointly learning different tasks on longitudinal data with missing values. The method uses Bayesian variational inference to impute missing values and combine information of several views. This way, we can combine different data-views from different time-points in a common latent space and learn the relations between each ti…
National Tsing Hua University（国立清华大学）；MOST Joint Research Center for AI Technology and All Vista Healthcare（科技部人工智能技术与全视野医疗联合研究中心）；National Health Research Institutes（国家卫生研究院）
The ability to accurately detect onset of dementia is important in the treatment of the disease. Clinically, the diagnosis of Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI) patients are based on an integrated assessment of psychological tests and brain imaging such as positron emission tomography (PET) and anatomical magnetic resonance imaging (MRI). In this work using two different datasets, we propose a behavior score-embedded encoder network (BSEN) that integrates regularly adminstrated psychological tests information into the encoding procedure of representing subject's resting…
School of Engineering, Lakehead University（湖首大学工程学院）
Alzheimer's Disease (AD) is a severe brain disorder, destroying memories and brain functions. AD causes chronically, progressively, and irreversibly cognitive declination and brain damages. The reliable and effective evaluation of early dementia has become essential research with medical imaging technologies and computer-aided algorithms. This trend has moved to modern Artificial Intelligence (AI) technologies motivated by deeplearning success in image classification and natural language processing. The purpose of this review is to provide an overview of the latest research involving deep-lear…
BITS Pilani, K.K. Birla Goa Campus（Birla技术与科学学院皮拉尼校区 K.K. Birla果阿校区）；IEMN Université de Lille（里尔大学微电子与纳米技术研究所）
Alzheimers disease is a fatal progressive brain disorder that worsens with time. It is high time we have inexpensive and quick clinical diagnostic techniques for early detection and care. In previous studies, various Machine Learning techniques and Pre-trained Deep Learning models have been used in conjunction with the extraction of various acoustic and linguistic features. Our study focuses on three models for the classification task in the ADReSS (The Alzheimers Dementia Recognition through Spontaneous Speech) 2021 Challenge. We use the well-balanced dataset provided by the ADReSS Challenge …
Alzheimer's disease is estimated to affect around 50 million people worldwide and is rising rapidly, with a global economic burden of nearly a trillion dollars. This calls for scalable, cost-effective, and robust methods for detection of Alzheimer's dementia (AD). We present a novel architecture that leverages acoustic, cognitive, and linguistic features to form a multimodal ensemble system. It uses specialized artificial neural networks with temporal characteristics to detect AD and its severity, which is reflected through Mini-Mental State Exam (MMSE) scores. We first evaluate it on the ADRe…
Dementia classification from spontaneous speech using wrapper-based feature selection
Faculty of Information Technology, University of Jyväskylä（信息科技学院，于韦斯屈莱大学）；Public Health Programme, Folkhälsan Research Center（公共卫生计划，福尔哈兰研究中心）；Faculty of Sport and Health Sciences and Gerontology Research Center, University of Jyväskylä（体育与健康科学学院及老年学研究中心，于韦斯屈莱大学）；Wellbeing Services County of Central Finland（中央芬兰福祉服务县）
Dementia is a leading cause of diseases for the elderly. Early diagnosis is very important for the elderly living with dementias. In this paper, we propose a method for dementia diagnosis by predicting MMSE score from finger-tapping measurement with machine learning pipeline. Based on measurement of finger tapping movement, the pipeline is first to select finger-tapping attributes with copula entropy and then to predict MMSE score from the selected attributes with predictive models. Experiments on real world data show that the predictive models such developed present good prediction performanc…
Predictive Modelling of Natural Medicinal Compounds for Alzheimer disease Using Machine Learning and Cheminformatics
Department of Biomedical Engineering（生物医学工程系）；Hamdard University（哈姆达德大学）；Department of Computer Science and Engineering（计算机科学与工程系）；Malardalen University（马尔默大学）
Flexible and Explainable Graph Analysis for EEG-based Alzheimer's Disease Classification
Stevens Institute of Technology（史蒂文斯理工学院）；Semcer Center for Healthcare Innovation（塞姆瑟医疗创新中心）；New Jersey Institute of Technology（新泽西理工学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Institute of Brain Science, College of Medicine, National Yang-Ming Chiao Tung University（国立阳明交通大学医学院脑科学研究所）
Alzheimer's disease (AD) is the most prevalent form of dementia. Traditional methods cannot achieve efficient and accurate diagnosis of AD. In this paper, we introduce a novel method based on dynamic functional connectivity (dFC) that can effectively capture changes in the brain. We compare and combine four different types of features including amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), dFC and the adjacency matrix of different brain structures between subjects. We use graph convolution network (GCN) which consider the similarity of brain structure between pati…
