Predicting Parkinson’s Disease Motor Progression Using Clinical and Digital Data
Objective: To define motor progression subphenotypes in Parkinson’s disease (PD) and evaluates the predictive power of clinical and smartphone data in distinguishing slow from fast…Efficacy and Feasibility of Telemedicine-Based Dietary Intervention vs. Outpatient Supervision for Drug-Resistant Multiple System Atrophy in Postmenopausal Women During COVID-19
Objective: This study aimed to evaluate the feasibility, safety, and patient satisfaction of initiating and following up on MAD via telemedicine using the Telegram app,…Using Chronic DBS Brain Sensing Data to Develop an AI-based Engine for Clinical Insights
Objective: Our aim is to build an AI-based algorithm engine to deliver brain sensing-based data insights from deep brain stimulation (DBS) therapy systems that are…Improving Automatic Speech Recognition for Speakers with Parkinson’s disease
Objective: 1. Obtain high-fidelity, speech data from 400 people with Parkinson’s (PWP) to train automatic speech recognition (ASR) systems.2. Develop and implement procedures to recruit,…Quantifying Bradykinesia in Real-world Practice: A Clinician-friendly Video Analysis Tool for Parkinson’s Disease
Objective: To develop and validate a video-based automated analysis tool for the objective and quantitive assessment of bradykinesia in Parkinson’s disease (PD). Background: Currently, evaluating…Joint Prediction of Motor and Non-motor Deep Brain Stimulation Outcomes using Quantitative Susceptibility Mapping
Objective: To jointly estimate motor and non-motor outcomes of deep brain stimulation using presurgical quantitative susceptibility maps. Background: Parkinson’s disease (PD) patients with motor complications…Detection of Early Stage Parkinson’s Disease Using Convolutional Neural Network Models and Wearable Sensors from the Six Minute Walk Test
Objective: To evaluate the efficacy of convolutional neural network (CNN) models combined with six-minute walk test (6MWT) data collected via wearable sensors for distinguishing early-stage…Detection of novel acoustic biomarkers among Parkinson’s disease patients via an explainable machine learning model
Objective: Our aim was to develop and compare machine learning (ML) algorithms for identification of Parkinson’s disease (PD) patients via acoustic analysis of vowel articulation…Automatic Intelligibility Rating in Parkinson’s Disease: A Multilingual Approach
Objective: To investigate the validity and robustness of an automatically generated intelligibility score in Parkinson’s disease (PD) across multiple languages. Background: 90% of people with…Traditional Deep Brain Stimulation Programming versus Automated Image-Guided Algorithm in Patients with Parkinson’s Disease
Objective: To compare traditional initial deep brain stimulation (DBS) programming with artificial intelligence (AI)-assisted automated image-guided DBS programming algorithm. Background: DBS programming is a complex,…
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