Alzheimer's disease (AD) is a devastating neurodegenerative condition that precedes progressive and irreversible dementia; thus, predicting its progression over time is vital for clinical diagnosis and treatment. Numerous studies have implemented structural magnetic resonance imaging (MRI) to model AD progression, focusing on three integral aspects: (i) temporal variability, (ii) incomplete observations, and (iii) temporal geometric characteristics. However, deep learning-based approaches regarding data variability and sparsity have yet to consider inherent geometrical properties sufficiently. The ordinary differential equation-based geometric modeling method (ODE-RGRU) has recently emerged as a promising strategy for modeling time-series data by intertwining a recurrent neural network and an ODE in Riemannian space. Despite its achievements, ODE-RGRU encounters limitations when extrapolating positive definite symmetric metrics from incomplete samples, leading to feature reverse occurrences that are particularly problematic, especially within the clinical facet. Therefore, this study proposes a novel geometric learning approach that models longitudinal MRI biomarkers and cognitive scores by combining three modules: topological space shift, ODE-RGRU, and trajectory estimation. We have also developed a training algorithm that integrates manifold mapping with monotonicity constraints to reflect measurement transition irreversibility. We verify our proposed method's efficacy by predicting clinical labels and cognitive scores over time in regular and irregular settings. Furthermore, we thoroughly analyze our proposed framework through an ablation study.
Multi-Task Learning with Feature-Similarity Laplacian Graphs for Predicting Alzheimer's Disease Progression
School of Software（软件学院）；Yunnan University（云南大学）；Department of Computer Science（计算机科学系）；The University of Sheffield（谢菲尔德大学）；Department of Computing（计算系）；Xi’an JiaoTong-Liverpool University（西安交通大学利物浦大学）；School of Medical Technology（医学技术学院）；Beijing Institute of Technology（北京理工大学）
PROMISE-AD: Progression-aware Multi-horizon Survival Estimation for Alzheimer's Disease Progression and Dynamic Tracking
Department of Radiology and Biomedical Imaging, Yale School of Medicine（放射科与生物医学成像系，耶鲁医学院）；Department of Radiology Oncology, Wake Forest University School of Medicine（放射肿瘤科，沃杰福大学医学院）；Department of Applied Mathematics & Statistics and Department of Computer Science, Stony Brook University（应用数学与统计系和计算机科学系，史泰文布鲁克大学）
Flexible Multimodal Neuroimaging Fusion for Alzheimer's Disease Progression Prediction
Department of Computer Science and Engineering（计算机科学与工程系）；Department of Biomedical Informatics（生物医学信息学系）；Department of Neurology（神经病学系）；Translational Data Analytics Institute（转化数据分析研究所）
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 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)…
Artificial Intelligence for Personalized Prediction of Alzheimer's Disease Progression: A Survey of Methods, Data Challenges, and Future Directions
Department of Industrial Engineering and Management Systems University of Central Florida（工业工程与管理系统系 佛罗里达中央大学）；Institute for Simulation and Training University of Central Florida（模拟与培训研究所 佛罗里达中央大学）；Department of Internal Medicine University of Central Florida（内科医学系 佛罗里达中央大学）；Department of Statistics and Data Science University of Central Florida（统计学与数据科学系 佛罗里达中央大学）
Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer's Disease Continuum
University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德河谷分校）；University of Pittsburgh（匹兹堡大学）；Eli and Lilly Company（礼来公司）；Washington University in St. Louis（圣路易斯华盛顿大学）
Deformation-aware Temporal Generation for Early Prediction of Alzheimers Disease
the College of Computer Science and Technology,Huaqiao University（华侨大学计算机科学与技术学院）；the Key Laboratory of Computer Vision and Machine Learning in Fujian Province（福建省计算机视觉与机器学习重点实验室）
Regional Deep Atrophy: a Self-Supervised Learning Method to Automatically Identify Regions Associated With Alzheimer's Disease Progression From Longitudinal MRI
University of Pennsylvania（宾夕法尼亚大学）；Lund University（隆德大学）；Institut National de la Santé et de la Recherche Médicale (INSERM)（法国国家健康与医学研究院）
提出Regional Deep Atrophy（RDA）方法，结合时间推断、可变形配准网络与注意力机制，在保持预测精度的同时提升可解释性，突出与阿尔茨海默病进展相关的纵向MRI变化区域。
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:…
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen University（深圳大学）；Renmin University of China（中国人民大学）；Central South University（中南大学）
Alzheimer's disease is characterized by alterations of the brain's structural and functional connectivity during its progressive degenerative processes. Existing auxiliary diagnostic methods have accomplished the classification task, but few of them can accurately evaluate the changing characteristics of brain connectivity. In this work, a prior guided adversarial representation learning and hypergraph perceptual network (PGARL-HPN) is proposed to predict abnormal brain connections using triple-modality medical images. Concretely, a prior distribution from the anatomical knowledge is estimated…
Early diagnosis of Alzheimer's disease (AD) is essential in preventing the disease's progression. Therefore, detecting AD from neuroimaging data such as structural magnetic resonance imaging (sMRI) has been a topic of intense investigation in recent years. Deep learning has gained considerable attention in Alzheimer's detection. However, training a convolutional neural network from scratch is challenging since it demands more computational time and a significant amount of annotated data. By transferring knowledge learned from other image recognition tasks to medical image classification, trans…
ADAM: An AI Reasoning and Bioinformatics Model for Alzheimer's Disease Detection and Microbiome-Clinical Data Integration
UMass Chan Medical School（马萨诸塞大学陈医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Harrisburg University of Science and Technology（哈里斯堡科技大学）；University of Massachusetts Chan Medical School（马萨诸塞大学陈医学院）
CognitiveTwin: Robust Multi-Modal Digital Twins for Predicting Cognitive Decline in Alzheimer's Disease
Department of Mechanical, Industrial & Manufacturing Eng.（机械、工业与制造工程系）；The University of Toledo（托莱多大学）；Department of Industrial Eng. and Mngt. Systems（工业工程与管理系统系）；University of Central Florida（中央佛罗里达大学）；Department of Statistics and Data Science（统计与数据科学系）；Department of Internal Medicine（内科医学系）
Enhancing Early Alzheimer Disease Detection through Big Data and Ensemble Few-Shot Learning
Robotics and Internet of Things Laboratory, Prince Sultan University（王子苏大学机器人与物联网实验室）；RIADI Laboratory, National School of Computer Science, University of Manouba（RIADI实验室，国家计算机科学学院，曼努巴大学）
Collaborative Attention and Consistent-Guided Fusion of MRI and PET for Alzheimer's Disease Diagnosis
School of Software Yunnan University（软件学院 云南大学）；Department of Computer Science The University of Sheffield（计算机科学系 剑桥大学）；Department of Computing Xian JiaoTong-Liverpool University（计算系 西交利物浦大学）
Brain-Aware Readout Layers in GNNs: Advancing Alzheimer's early Detection and Neuroimaging
AI Graduate School, Gwangju Institute of Science and Technology（光州科学技术院人工智能研究生院）；Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea（韩国天主教大学医学院首尔圣玛丽医院）；Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea（韩国天主教大学医学院汝矣岛圣玛丽医院）
