Multimodal Gait Recognition for Neurodegenerative Diseases
Qingdao University（青岛大学）；Ocean University of China（中国海洋大学）；Queen Mary University of London（伦敦玛丽女王大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Hohai University（河海大学）；University of Leicester（莱斯特大学）
Aite Zhao, Jianbo Li, Junyu Dong, Lin Qi, Qianni Zhang, Ning Li, Xin Wang, Huiyu Zhou
In recent years, single modality based gait recognition has been extensively explored in the analysis of medical images or other sensory data, and it is recognised that each of the established approaches has different strengths and weaknesses. As an important motor symptom, gait disturbance is usually used for diagnosis and evaluation of diseases; moreover, the use of multi-modality analysis of the patient's walking pattern compensates for the one-sidedness of single modality gait recognition methods that only learn gait changes in a single measurement dimension. The fusion of multiple measurement resources has demonstrated promising performance in the identification of gait patterns associated with individual diseases. In this paper, as a useful tool, we propose a novel hybrid model to learn the gait differences between three neurodegenerative diseases, between patients with different severity levels of Parkinson's disease and between healthy individuals and patients, by fusing and aggregating data from multiple sensors. A spatial feature extractor (SFE) is applied to generating representative features of images or signals. In order to capture temporal information from the two modality data, a new correlative memory neural network (CorrMNN) architecture is designed for extracting temporal features. Afterwards, we embed a multi-switch discriminator to associate the observations with individual state estimations. Compared with several state-of-the-art techniques, our proposed framework shows more accurate classification results.
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）
Domain-Specific Foundation Model Improves AI-Based Analysis of Neuropathology
Windreich Department of AI and Human Health（AI与人类健康部门）；Icahn School of Medicine at Mount Sinai（辛克医学院（梅奥医院））；Hasso Plattner Institute for Digital Health at Mount Sinai（梅奥医院数字健康研究所）；Department of Neurology（神经病学部门）；Department of Pathology（病理学部门）；Columbia University（哥伦比亚大学）；Memorial Sloan-Kettering Cancer Center（纪念斯隆凯特林癌症中心）；Department of Neuroscience（神经科学部门）；Mayo Clinic College of Medicine（梅奥诊所医学院）；University of Texas Southwestern Medical Center（德克萨斯西南医学中心）；Peter O’Donnell Jr. Brain Institute（彼得·奥多尼尔 Jr. 脑研究所）；VA Boston Healthcare System（波士顿退伍军人医疗系统）
PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies
Sorbonne University（索邦大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共医疗集团）；Inria（法国国家信息与自动化研究所）；Paris Brain Institute - ICM（巴黎大脑研究所 - ICM）；PSL Research university（巴黎文理研究大学）；EPHE（高等研究实践学院）
A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis
Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY), Nanyang Technological University（南洋理工大学）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）
Imaging Biomarkers for Neurodegenerative Diseases from Detailed Segmentation of Medial Temporal Lobe Subregions on in vivo Brain MRI Using Upsampling Strategy Guided by High-resolution ex vivo MRI
University of Pennsylvania（宾夕法尼亚大学）；Siemens Healthineers（西门子医疗）；Lund University（隆德大学）；University of Castilla La Mancha（卡斯蒂利亚-拉曼恰大学）
SAND: The Challenge on Speech Analysis for Neurodegenerative Disease Assessment
National Research Council of Italy (CNR), Institute for High-Performance Computing and Networking (ICAR), Naples（意大利国家研究理事会（CNR）、高性能计算与网络研究所（ICAR）、那不勒斯）
University College London（伦敦大学学院）；University of Côte d’Azur（蔚蓝海岸大学）；Inria Sophia Antipolis（法国国家信息与自动化研究所索菲亚·安提波利斯中心）
We introduce Disease Knowledge Transfer (DKT), a novel technique for transferring biomarker information between related neurodegenerative diseases. DKT infers robust multimodal biomarker trajectories in rare neurodegenerative diseases even when only limited, unimodal data is available, by transferring information from larger multimodal datasets from common neurodegenerative diseases. DKT is a joint-disease generative model of biomarker progressions, which exploits biomarker relationships that are shared across diseases. Our proposed method allows, for the first time, the estimation of plausibl…
Detecting Daily Living Gait Amid Huntington's Disease Chorea using a Foundation Deep Learning Model
Tel Aviv University（特拉维夫大学）；Tel Aviv Sourasky Medical Center（特拉维夫苏拉斯基医疗中心）；Teachers College, Columbia University（哥伦比亚大学教师学院）；Wayne State University（韦恩州立大学）；Stony Brook University（石溪大学）；Rush University Medical Center（拉什大学医学中心）
Self-Supervised Cross-Encoder for Neurodegenerative Disease Diagnosis
School of Mathematics and Statistics, Glasgow, UK（数学与统计学学院，英国格拉斯哥）；Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK（计算机与信息科学系，斯特拉斯克莱德大学，英国格拉斯哥）
Zhejiang University of Technology（浙江工业大学）；University of California, Davis（加利福尼亚大学戴维斯分校）
Diffusion tensor imaging (DTI) has been used to study the effects of neurodegenerative diseases on neural pathways, which may lead to more reliable and early diagnosis of these diseases as well as a better understanding of how they affect the brain. We introduce an intelligent visual analytics system for studying patient groups based on their labeled DTI fiber tract data and corresponding statistics. The system's AI-augmented interface guides the user through an organized and holistic analysis space, including the statistical feature space, the physical space, and the space of patients over di…
This paper describes a new neuroimaging analysis toolbox that allows for the modeling of nonlinear effects at the voxel level, overcoming limitations of methods based on linear models like the GLM. We illustrate its features using a relevant example in which distinct nonlinear trajectories of Alzheimer's disease related brain atrophy patterns were found across the full biological spectrum of the disease. The open-source toolbox presented in this paper is available at https://github.com/imatge-upc/VNeAT.
ARMARecon: An ARMA Convolutional Filter based Graph Neural Network for Neurodegenerative Dementias Classification
IEEE International Symposium on Biomedical Imaging (ISBI) 2026（IEEE生物医学成像国际会议（ISBI）2026）；VSS Tejaswi Abburi；Ananya Singhal；Saurabh J. Shigwan；Nitin Kumar
Diagnosing Parkinson's disease is a complex task that requires the evaluation of several motor and non-motor symptoms. During diagnosis, gait abnormalities are among the important symptoms that physicians should consider. However, gait evaluation is challenging and relies on the expertise and subjectivity of clinicians. In this context, the use of an intelligent gait analysis algorithm may assist physicians in order to facilitate the diagnosis process. This paper proposes a novel intelligent Parkinson detection system based on deep learning techniques to analyze gait information. We used 1D co…
Cross-Corpus and Cross-domain Handwriting Assessment of NeuroDegenerative Diseases via Time-Series-to-Image Conversion
Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore MD, USA（电气与计算机工程系，约翰霍普金斯大学，巴尔的摩 MD，美国）；Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University, Baltimore MD, USA（精神病学与行为科学系，约翰霍普金斯大学，巴尔的摩 MD，美国）；Department of Neurology, The Johns Hopkins University, Baltimore MD, USA（神经病学系，约翰霍普金斯大学，巴尔的摩 MD，美国）
University of Connecticut（康涅狄格大学）；Arizona State University（亚利桑那州立大学）；Philips Research North America（飞利浦北美研究院）；Nanjing Brain Hospital（南京脑科医院）
As our population ages, neurological impairments and degeneration of the musculoskeletal system yield gait abnormalities, which can significantly reduce quality of life. Gait rehabilitative therapy has been widely adopted to help patients maximize community participation and living independence. To further improve the precision and efficiency of rehabilitative therapy, more objective methods need to be developed based on sensory data. In this paper, an algorithmic framework is proposed to provide classification of gait disorders caused by two common neurological diseases, stroke and Parkinson'…
Automated deep learning segmentation of high-resolution 7 T postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases
A Multimodal In Vitro Diagnostic Method for Parkinson's Disease Combining Facial Expressions and Behavioral Gait Data
School of Mathematics and Computer Sciences, Nanchang University, Nanchang, China（南昌大学数学与计算机科学学院）；Yichun University, Yichun, China（宜春大学）；Nanchang University Second Affiliated Hospital, Nanchang, China（南昌大学第二附属医院）
Weill Cornell Medical College, Cornell University（康奈尔大学威尔康奈尔医学院）；University of Connecticut（康涅狄格大学）；Feinberg School of Medicine, Northwestern University（西北大学范伯格医学院）；Michigan State University（密歇根州立大学）
Parkinson's Disease (PD) is one of the most prevalent neurodegenerative diseases that affects tens of millions of Americans. PD is highly progressive and heterogeneous. Quite a few studies have been conducted in recent years on predictive or disease progression modeling of PD using clinical and biomarkers data. Neuroimaging, as another important information source for neurodegenerative disease, has also arisen considerable interests from the PD community. In this paper, we propose a deep learning method based on Graph Convolutional Networks (GCN) for fusing multiple modalities of brain images …
Sathyabama University（萨蒂亚巴马大学）；Alpha College of Engineering（阿尔法工程学院）
Nuclear image has emerged as a promising research work in medical field. Images from different modality meet its own challenge. Positron Emission Tomography (PET) image may help to precisely localize disease to assist in planning the right treatment for each case and saving valuable time. In this paper, a novel approach of Spatial Fuzzy C Means (PET SFCM) clustering algorithm is introduced on PET scan image datasets. The proposed algorithm is incorporated the spatial neighborhood information with traditional FCM and updating the objective function of each cluster. This algorithm is implemented…
Parkinson's disease (PD) is a progressive neurological disorder primarily affecting motor function resulting in tremor at rest, rigidity, bradykinesia, and postural instability. The physical severity of PD impairments can be quantified through the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), a widely used clinical rating scale. Accurate and quantitative assessment of disease progression is critical to developing a treatment that slows or stops further advancement of the disease. Prior work has mainly focused on dopamine transport neuroimaging for diagnosis or…
Accumulating evidence has shown that iron is involved in the mechanism underlying many neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease and Huntington's disease. Abnormal (higher) iron accumulation has been detected in the brains of most neurodegenerative patients, especially in the basal ganglia region. Presence of iron leads to changes in MR signal in both magnitude and phase. Accordingly, tissues with high iron concentration appear hypo-intense (darker than usual) in MR contrasts. In this report, we proposed an improved binary hypointensity description and a nove…
4DLoG: Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model
Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Radiology（放射学系）；Department of Electrical and Computer Engineering, Department of Computer Science（电气与计算机工程系、计算机科学系）
CARE-PD: A Multi-Site Anonymized Clinical Dataset for Parkinson's Disease Gait Assessment
University of Toronto（多伦多大学）；Vector Institute（向量研究所）；KITE Research Institute-UHN（KITE研究 institute-UHN）；University of Strasbourg（斯特拉斯堡大学）；University Hospitals of Strasbourg（斯特拉斯堡大学医院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Federal University of ABC（巴西ABC联邦大学）；KU Leuven（鲁文大学）；Hasselt University（哈瑟尔特大学）；Emory University（埃默里大学）；University of Bristol（布里斯托尔大学）
BrainMAP: Multimodal Graph Learning For Efficient Brain Disease Localization
School of Computing Technologies, RMIT University（计算技术学院，拉筹纳斯大学）；Institute of Innovation, Science and Sustainability, Federation University Australia（创新、科学与可持续性研究所，联邦大学澳大利亚）
ConnectomeDiffuser: Generative AI Enables Brain Network Construction from Diffusion Tensor Imaging
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究所）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）；Department of Mathematics, Hong Kong Baptist University（香港 Baptist大学数学系）
Parkinson's disease (PD) is a neurological disorder that has a variety of observable motor-related symptoms such as slow movement, tremor, muscular rigidity, and impaired posture. PD is typically diagnosed by evaluating the severity of motor impairments according to scoring systems such as the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Automated severity prediction using video recordings of individuals provides a promising route for non-intrusive monitoring of motor impairments. However, the limited size of PD gait data hinders model ability and clinical po…
University College London（伦敦大学学院）；Inria（法国国家信息与自动化研究所）；Université Côte d'Azur（蔚蓝海岸大学）
Here we present DIVE: Data-driven Inference of Vertexwise Evolution. DIVE is an image-based disease progression model with single-vertex resolution, designed to reconstruct long-term patterns of brain pathology from short-term longitudinal data sets. DIVE clusters vertex-wise biomarker measurements on the cortical surface that have similar temporal dynamics across a patient population, and concurrently estimates an average trajectory of vertex measurements in each cluster. DIVE uniquely outputs a parcellation of the cortex into areas with common progression patterns, leading to a new signature…
School of Arts, Media and Engineering, Arizona State University (ASU)（亚利桑那州立大学艺术、媒体与工程学院）；School of Electrical, Computer and Energy Engineering, ASU（亚利桑那州立大学电气、计算机与能源工程学院）；Edson College of Nursing and Health Innovation, ASU（亚利桑那州立大学埃德森护理与健康创新学院）；College of Health Solutions, ASU（亚利桑那州立大学健康解决方案学院）
Application and use of deep learning algorithms for different healthcare applications is gaining interest at a steady pace. However, use of such algorithms can prove to be challenging as they require large amounts of training data that capture different possible variations. This makes it difficult to use them in a clinical setting since in most health applications researchers often have to work with limited data. Less data can cause the deep learning model to over-fit. In this paper, we ask how can we use data from a different environment, different use-case, with widely differing data distrib…
REMEMBER: Retrieval-based Explainable Multimodal Evidence-guided Modeling for Brain Evaluation and Reasoning in Zero- and Few-shot Neurodegenerative Diagnosis
Lausanne University Hospital (CHUV)（洛桑大学医院（CHUV））；University of Lausanne (UNIL)（洛桑大学（UNIL））；Vietnam National University（越南国立大学）；University of Science, Vietnam National University Ho Chi Minh City（越南国立大学胡志明市科学大学）；International University, Vietnam National University Ho Chi Minh City（越南国立大学胡志明市国际大学）
Benchmarking Skeleton-based Motion Encoder Models for Clinical Applications: Estimating Parkinson's Disease Severity in Walking Sequences
University of Toronto（多伦多大学）；Toronto Rehabilitation Institute – University Health Network（多伦多康复研究所 - 大学健康网络）；Vienna University of Technology（维也纳工业大学）；Center for Mental Health – University Health Network（心理健康中心 - 大学健康网络）
Freezing of Gait Prediction From Accelerometer Data Using a Simple 1D-Convolutional Neural Network -- 8th Place Solution for Kaggle's Parkinson's Freezing of Gait Prediction Competition
University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf（汉堡大学心脏与血管中心，汉堡埃彭多夫大学医学中心）
Courant Institute of Mathematical Sciences（库朗数学科学研究所）
Gait recognition is a term commonly referred to as an identification problem within the Computer Science field. There are a variety of methods and models capable of identifying an individual based on their pattern of ambulatory locomotion. By surveying the current literature on gait recognition, this paper seeks to identify appropriate metrics, devices, and algorithms for collecting and analyzing data regarding patterns and modes of ambulatory movement across individuals. Furthermore, this survey seeks to motivate interest in a broader scope of longitudinal analysis regarding the perturbations…
