Neural Network Models Predict Parkinson’s Disease Using Brief-action Motion Sensor Data (S21.002)

To develop and evaluate deep-learning models for predicting Parkinson’s disease using brief-action motion-sensor data from sit-to-stand and turning tasks.

Authors

Veronica Santini (ORCID: https://orcid.org/0000-0002-5308-168X)
Suhrud Panchawagh (ORCID: https://orcid.org/0000-0002-8606-4202)
Zion Zibly (ORCID: https://orcid.org/0000-0003-1718-0821)

Institutions

Yale University (US)

Publication Details

Journal: Neurology
Published: 2025-04-07
DOI: https://doi.org/10.1212/wnl.0000000000208480
Primary Topic: Neurological disorders and treatments
Type: article
Field-Weighted Citation Impact: 0.00

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article

Neural Network Models Predict Parkinson’s Disease Using Brief-action Motion Sensor Data (S21.002)

Veronica Santini, Suhrud Panchawagh, Zion Zibly

Apr 7, 2025

2 links

Neurology

Neurological disorders and treatments

article

Neural Network Models Predict Parkinson’s Disease Using Brief-action Motion Sensor Data (S21.002)

Veronica Santini, Suhrud Panchawagh, Zion Zibly

April 7, 2025article en

Abstract

To develop and evaluate deep-learning models for predicting Parkinson’s disease using brief-action motion-sensor data from sit-to-stand and turning tasks.

NeurologyVol. 104(7_Supplement_1)

Yale University (US)

Openalex Percentile: Top 89%

Neurological disorders and treatments

DOI Source OpenAlex

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Neural Network Models Predict Parkinson’s Disease Using Brief-action Motion Sensor Data (S21.002) — Veronica Santini, Suhrud Panchawagh, et al. · Neurology (2025) | TGRS Research Map | TGRS