Evaluating neurological disorders such as Parkinson's disease (PD) is a challenging task that requires the assessment of several motor and non-motor functions. In this paper, we present an end-to-end deep learning framework to measure PD severity in two important components, hand movement and gait, of the Unified Parkinson's Disease Rating Scale (UPDRS). Our method leverages on an Inflated 3D CNN trained by a temporal segment framework to learn spatial and long temporal structure in video data. We also deploy a temporal attention mechanism to boost the performance of our model. Further, motion boundaries are explored as an extra input modality to assist in obfuscating the effects of camera motion for better movement assessment. We ablate the effects of different data modalities on the accuracy of the proposed network and compare with other popular architectures. We evaluate our proposed method on a dataset of 25 PD patients, obtaining 72.3% and 77.1% top-1 accuracy on hand movement and gait tasks respectively.
Facial Expression-based Parkinson's Disease Severity Diagnosis via Feature Fusion and Adaptive Class Balancing
School of Mathematics and Computer Sciences（数学与计算机科学学院）；Nanchang University（南昌大学）；Yichun University（宜春大学）；Nanchang University Second Affiliated Hospital（南昌大学第二附属医院）
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…
Institute of Biomedical Engineering, University of Toronto（多伦多大学生物医学工程研究所）；Department of Psychiatry, University of Toronto（多伦多大学精神病学系）；Centre for Mental Health, UHN（大学健康网络心理健康中心）
Drug-induced parkinsonism affects many older adults with dementia, often causing gait disturbances. New advances in vision-based human pose-estimation have opened possibilities for frequent and unobtrusive analysis of gait in residential settings. This work leverages novel spatial-temporal graph convolutional network (ST-GCN) architectures and training procedures to predict clinical scores of parkinsonism in gait from video of individuals with dementia. We propose a two-stage training approach consisting of a self-supervised pretraining stage that encourages the ST-GCN model to learn about gai…
Multi Class Parkinson Disease Detection Based on Finger Tapping Using Attention Enhanced CNN BiLSTM
School of Computer Science（计算机科学学院）；Engineering University of Aizu, Fukushima, Japan（工程大学（Aizu），福岛，日本）；Department of Computer Science（计算机科学系）；Engineering Army University of Science（科学与技术陆军大学）
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…
Interpretable machine learning predicts Parkinson's disease severity using motion-corrected QSM MRI and multiband multiecho fMRI features
Lyda Hill Department of Bioinformatics（Lyda Hill 生物信息学系）；Department of Biomedical Engineering（生物医学工程系）；University of Texas Southwestern Medical Center, Dallas, Texas, USA（德克萨斯西南医学中心，德克萨斯州达拉斯）
University of Toronto（多伦多大学）；Toronto Rehabilitation Institute, University Health Network（大学健康网络多伦多康复研究所）；University of Ottawa（渥太华大学）
Objective: To apply deep learning pose estimation algorithms for vision-based assessment of parkinsonism and levodopa-induced dyskinesia (LID). Methods: Nine participants with Parkinson's disease (PD) and LID completed a levodopa infusion protocol, where symptoms were assessed at regular intervals using the Unified Dyskinesia Rating Scale (UDysRS) and Unified Parkinson's Disease Rating Scale (UPDRS). A state-of-the-art deep learning pose estimation method was used to extract movement trajectories from videos of PD assessments. Features of the movement trajectories were used to detect and estim…
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）
Parkinsons disease, PD, is a chronic condition that affects motor skills and includes symptoms like tremors and rigidity. The current diagnostic procedure uses patient assessments to evaluate symptoms and sometimes a magnetic resonance imaging or MRI scan. However, symptom variations cause inaccurate assessments, and the analysis of MRI scans requires experienced specialists. This research proposes to accurately diagnose PD severity with deep learning by combining symptoms data and MRI data from the Parkinsons Progression Markers Initiative database. A new hybrid model architecture was impleme…
Pose-based Tremor Type and Level Analysis for Parkinson's Disease from Video
Durham University（杜伦大学）；University of Glasgow（格拉斯哥大学）；Newcastle University（纽卡斯尔大学）；National Institute for Health and Care Research Newcastle Biomedical Research Centre（国家健康与护理研究所纽卡斯尔生物医学研究中心）；The Newcastle upon Tyne Hospitals NHS Foundation Trust（泰恩河畔纽卡斯尔国民保健服务基金会信托医院）
Parkinson's Disease (PD) is a neurological disorder that affects facial movements and non-verbal communication. Patients with PD present a reduction in facial movements called hypomimia which is evaluated in item 3.2 of the MDS-UPDRS-III scale. In this work, we propose to use facial expression analysis from face images based on affective domains to improve PD detection. We propose different domain adaptation techniques to exploit the latest advances in face recognition and Face Action Unit (FAU) detection. The principal contributions of this work are: (1) a novel framework to exploit deep face…
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…
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）
AI WALKUP: A Computer-Vision Approach to Quantifying MDS-UPDRS in Parkinson's Disease
Key Lab of Image Processing and Intelligent Control, Ministry of Education（教育部图像处理与智能控制重点实验室）；School of Artificial Intelligence and Automation（人工智能与自动化学院）；Huazhong University of Science and Technology（华中科技大学）
TraMP-LLaMA: Generative Interpretability with Decoupled Instruction Tuning for Facial Expression Quality Assessment
School of Computer Science University of Bristol（布里斯托大学计算机科学学院）；Translational Health Sciences University of Bristol（布里斯托大学转化健康科学学院）；School of Computing and Communications Lancaster University（兰卡斯特大学计算与通信学院）
Azerbaijan State Oil and Industry University（阿塞拜疆石油与工业大学）；Istanbul University Cerrahpasa Faculty of Medicine Hospital（伊斯坦布尔大学切拉帕萨医学院附属医院）
In the world, about 7 to 10 million elderly people are suffering from Parkinson's Disease (PD) disease. Parkinson's disease is a common neurological degenerative disease, and its clinical characteristics are Tremors, rigidity, bradykinesia, and decreased autonomy. Its clinical manifestations are very similar to Multiple System Atrophy (MSA) disorders. Studies have shown that patients with Parkinson's disease often reach an irreparable situation when diagnosed, so As Parkinson's disease can be distinguished from MSA disease and get an early diagnosis, people are constantly exploring new methods…
Interpretable and Granular Video-Based Quantification of Motor Characteristics from the Finger Tapping Test in Parkinson Disease
Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center（神经科、帕金森病和运动障碍专家中心、大脑、认知与行为研究所、拉德堡德大学医学中心）；Department of Artificial Intelligence, Donders Institute for Brain, Cognition and Behaviour, Radboud University（人工智能系、大脑、认知与行为研究所、拉德堡德大学）
A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos
Foundation for Research and Technology Hellas (FORTH)（希腊研究与技术基金会）；University of Crete（克里特大学）；University Hospital of Heraklion（伊拉克利翁大学医院）；Patras University Hospital（帕特雷大学医院）；University of Ioannina（约阿尼纳大学）
University of Southern California（南加州大学）；Chang Gung Memorial Hospital（长庚纪念医院）；University of Pennsylvania Perelman School of Medicine（宾夕法尼亚大学佩雷尔曼医学院）；Chang Gung University（长庚大学）；Amsterdam UMC, Vrije Universiteit Amsterdam（阿姆斯特丹大学医学中心，阿姆斯特丹自由大学）
There is great interest in developing radiological classifiers for diagnosis, staging, and predictive modeling in progressive diseases such as Parkinson's disease (PD), a neurodegenerative disease that is difficult to detect in its early stages. Here we leverage severity-based meta-data on the stages of disease to define a curriculum for training a deep convolutional neural network (CNN). Typically, deep learning networks are trained by randomly selecting samples in each mini-batch. By contrast, curriculum learning is a training strategy that aims to boost classifier performance by starting wi…
A Symmetric Regressor for MRI-Based Assessment of Striatal Dopamine Transporter Uptake in Parkinson's Disease With Enhanced Uncertainty Estimation
Hankuk University of Foreign Studies（韩国外国语大学）；Seoul National University Bundang Hospital（首尔大学盆唐医院）；Seoul National University College of Medicine（首尔大学医学院）
Massachusetts Institute of Technology（麻省理工学院）；Yale University（耶鲁大学）；Massachusetts General Hospital（麻省总医院）
For many movement disorders, such as Parkinson's disease and ataxia, disease progression is visually assessed by a clinician using a numerical disease rating scale. These tests are subjective, time-consuming, and must be administered by a professional. This can be problematic where specialists are not available, or when a patient is not consistently evaluated by the same clinician. We present an automated method for quantifying the severity of motion impairment in patients with ataxia, using only video recordings. We consider videos of the finger-to-nose test, a common movement task used as pa…
University of California, Berkeley（加利福尼亚大学伯克利分校）；International Computer Science Institute（国际计算机科学研究所）；University of California, Davis（加利福尼亚大学戴维斯分校）
Many diseases are classified based on human-defined rubrics that are prone to bias. Supervised neural networks can automate the grading of retinal fundus images, but require labor-intensive annotations and are restricted to the specific trained task. Here, we employed an unsupervised network with Non-Parametric Instance Discrimination (NPID) to grade age-related macular degeneration (AMD) severity using fundus photographs from the Age-Related Eye Disease Study (AREDS). Our unsupervised algorithm demonstrated versatility across different AMD classification schemes without retraining, and achiev…
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）
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 measure…
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）
