A comprehensive view of factors associated with AD/ADRD will significantly aid in studies to develop new treatments for AD/ADRD and identify high-risk populations and patients for prevention efforts. In our study, we summarized the risk factors for AD/ADRD by reviewing existing meta-analyses and review articles on risk and preventive factors for AD/ADRD. In total, we extracted 477 risk factors in 10 categories from 537 studies. We constructed an interactive knowledge map to disseminate our study results. Most of the risk factors are accessible from structured Electronic Health Records (EHRs), and clinical narratives show promise as information sources. However, evaluating genomic risk factors using RWD remains a challenge, as genetic testing for AD/ADRD is still not a common practice and is poorly documented in both structured and unstructured EHRs. Considering the constantly evolving research on AD/ADRD risk factors, literature mining via NLP methods offers a solution to automatically update our knowledge map.
Usher Institute, Edinburgh Medical School, The University of Edinburgh（爱丁堡大学爱丁堡医学院厄舍研究所）；Carnegie Mellon University（卡内基梅隆大学）；Information Technologies Institute, CERTH（希腊研究与技术基金会信息技术研究所）
This Signal Processing Grand Challenge (SPGC) targets a difficult automatic prediction problem of societal and medical relevance, namely, the detection of Alzheimer's Dementia (AD). Participants were invited to employ signal processing and machine learning methods to create predictive models based on spontaneous speech data. The Challenge has been designed to assess the extent to which predictive models built based on speech in one language (English) generalise to another language (Greek). To the best of our knowledge no work has investigated acoustic features of the speech signal in multiling…
Probability-Invariant Random Walk Learning on Gyral Folding-Based Cortical Similarity Networks for Alzheimer's and Lewy Body Dementia Diagnosis
Department of Computer Science and Engineering, University of Texas at Arlington, United States（德克萨斯理工大学计算机科学与工程系）；School of Computing, University of Georgia, United States（佐治亚大学计算机学院）；Department of Psychiatry, University of Cambridge, United Kingdom（剑桥大学精神病学系）；Sheffield Institute for Translational Neuroscience, University of Sheffield, United Kingdom（谢菲尔德大学转化神经科学研究所）
Balancing Caregiving and Self-Care: Exploring Mental Health Needs of Alzheimer's and Dementia Caregivers
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Indiana University Indianapolis（印第安纳大学印第安纳波利斯分校）；University of Massachusetts Amherst（马萨诸塞大学阿姆赫斯特分校）
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:…
Swin-BERT: A Feature Fusion System designed for Speech-based Alzheimer's Dementia Detection
College of Artificial Intelligence, Dalian Maritime University（大连海事大学人工智能学院）；University of Sheffield（谢菲尔德大学）；College of Information Science and Technology, Dalian Maritime University（大连海事大学信息科学技术学院）
A Taxonomy of Mental Health and Technology Needs for Alzheimer's and Dementia Caregivers
Siebel School of Computing and Data Science（Siebel计算与数据科学学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Department of Psychology（心理学系）；Illinois Neurological Institute（伊利诺伊神经科学研究所）；Department of Human-Centered Computing（以人为中心计算系）；Manning College of Information and Computer Sciences（马歇尔大学信息与计算机科学学院）
Linguistic and Audio Embedding-Based Machine Learning for Alzheimer's Dementia and Mild Cognitive Impairment Detection: Insights from the PROCESS Challenge
Singapore University of Technology and Design（新加坡科技设计大学）；Hochschule für Musik Detmold（音乐学院Detmold）
Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregivers
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Indiana University Indianapolis（印第安纳大学印第安纳波利斯分校）；University of Massachusetts Amherst（马萨诸塞大学阿姆赫斯特分校）
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）
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…
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…
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）
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）
Predictive Analytics for Dementia: Machine Learning on Healthcare Data
Faculty of Science \& Technology American International University-Bangladesh Dhaka, Bangladesh；Faculty of Information Science \& Technology Multimedia University Melaka, Malaysia；Faculty of Computing \& Informatics Multimedia University Cyberjaya, Malaysia；Department of CSE University of Asia Pacific, Bangladesh, Dhaka, Bangladesh
University of Tennessee（田纳西大学）；Miami University（迈阿密大学）；University of Tennessee Health Science Center（田纳西大学健康科学中心）；University of Iowa（爱荷华大学）
People with Alzheimer's disease and related dementias (ADRD) often show the problem of repetitive questioning, which brings a great burden on persons with ADRD (PwDs) and their caregivers. Conversational robots hold promise of coping with this problem and hence alleviating the burdens on caregivers. In this paper, we proposed a partially observable markov decision process (POMDP) model for the PwD-robot interaction in the context of repetitive questioning, and used Q-learning to learn an adaptive conversation strategy (i.e., rate of follow-up question and difficulty of follow-up question) towa…
xEEGNet: Towards Explainable AI in EEG Dementia Classification
Department of Neuroscience, University of Padua（帕多瓦大学神经科学系）；Padua Neuroscience Center（帕多瓦神经科学中心）；Department of Information Engineering, University of Padua（帕多瓦大学信息工程系）；Department of Physics and Astronomy, University of Padua（帕多瓦大学物理与天文学系）；Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais)（瑞士西部应用科学大学信息系统研究所）
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…
Alzheimer's Disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia. Early diagnosis is critical for patients to benefit from potential intervention and treatment. The retina has been hypothesized as a diagnostic site for AD detection owing to its anatomical connection with the brain. Developed AI models for this purpose have yet to provide a rational explanation about the decision and neither infer the stage of disease's progression. Along this direction, we propose a novel model-agnostic explainable-AI framework, called Granular Neuron-level Explainer (LAVA)…
Universidade Federal de Pernambuco（伯南布哥联邦大学）；Universidade de Pernambuco（伯南布哥大学）；North Carolina State University（北卡罗来纳州立大学）
Alzheimer's disease is the most common cause of dementia, yet hard to diagnose precisely without invasive techniques, particularly at the onset of the disease. This work approaches image analysis and classification of synthetic multispectral images composed by diffusion-weighted magnetic resonance (MR) cerebral images for the evaluation of cerebrospinal fluid area and measuring the advance of Alzheimer's disease. A clinical 1.5 T MR imaging system was used to acquire all images presented. The classification methods are based on multilayer perceptrons and Kohonen Self-Organized Map classifiers.…
