Selecting an Imaging Biomarker for a Multi-Arm Multi-Stage Trial of Disease-Modifying Treatments for Parkinson’s: The EJS ACT-PD Experience
Objective: To report on the selection of an imaging biomarker for a sub-study in a multi-arm, multi-stage (MAMS) platform trial for disease-modifying therapies (DMTs) in…White matter abnormalities in motor subtypes of Parkinson’s disease
Objective: To evaluate white matter microstructural integrity in Tremor dominant Parkinson’s disease (TDPD) and postural instability and gait disturbance (PIGD) subtypes of PD to ascertain…Automated Imaging Differentiation of Parkinsonism (AIDP): A Prospective 21-site Trial within the Parkinson’s Study Group
Objective: To prospectively test the performance of Automated Imaging Differentiation of Parkinsonism (AIDP), a machine learning based diagnostic biomarker software for Parkinson’s disease (PD), multiple system atrophy Parkinsonian…Deep brain stimulation Intraoperative imaging: False errors in the era of artificial intelligence
Objective: To assess the precision of Deep Brain Stimulation (DBS) electrode placement within Subthalamic nucleus (STN) using Advanced Imaging and artificial intelligence, considering the challenges…Imaging the Nigrostriatal Pathway in Patients with Idiopathic Normal Pressure Hydrocephalus and Parkinsonism
Objective: To investigate the nigrostriatal pathway in iNPH patients with clinical parkinsonism, employing dopaminergic transporter (DAT) and nigrosome imaging. Background: Patients with idiopathic Normal Pressure…Prediction of The Monopolar Review in Deep Brain Stimulation for Parkinson’s Disease using Imaging
Objective: To build an AI model that can predict the monopolar review in deep brain stimulation (DBS) for Parkinson’s disease (PD) based on imaging. Background:…Accuracy of AI-driven automated diagnostic software analyzing Susceptibility Map-Weighted Imaging to Differentiate Neurodegenerative from Non-neurodegenerative Parkinsonism
Objective: To determine the accuracy of AI-driven automated diagnostic software analyzing susceptibility map-weighted imaging (SMWI) to distinguish neurodegenerative from non-neurodegenerative parkinsonism in patients who had…From Images to Insights: Subtyping Parkinson’s Disease Using Unsupervised Learning on MRI Data
Objective: To explore the heterogeneity of Parkinson's disease (PD) using unsupervised clustering of neuroimaging data, aiming to identify distinct subtypes based on volumetric features. Background:…Responsive morphometric fingerprints in deep brain stimulation for Parkinson’s disease
Objective: In this study, we identified differential network characteristics based on graph theory of structural covariance network (SCN) between responders and non-responders of DBS. Background:…Diffusion MRI and Machine Learning Distinguish Alzheimer’s Disease and Dementia with Lewy Bodies
Objective: This study reports recent updates in the development of a support vector machine learning model to discriminate between dementia variants (i.e., Alzheimer’s disease (AD)…
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