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Development of Digital Diagnostic Devices for Parkinson's Disease
Sponsor: machineMD AG
Summary
In this project, ocular motor, pupil and gait data in people with Parkinson's disease (PD) will be collected in order to develop machine learning models for the diagnosis and monitoring of PD. With this, the investigators aim to advance the state of the art in PD diagnosis and monitoring. By integrating the principles of machine learning with high-quality sensor data, more accurate and earlier diagnosis could potentially be achieved. Ocular motor and pupil data will be collected with the standard clinical examination and with neos, a medical device approved for objective ocular motor and pupil measurement. Gait will be collected using an IMU sensor and GaitQ senti, a consumer device that allows for an objective and continuous remote gait monitoring.
Key Details
Gender
All
Age Range
Any - Any
Study Type
OBSERVATIONAL
Enrollment
100
Start Date
2024-10-01
Completion Date
2026-06-30
Last Updated
2025-10-07
Healthy Volunteers
Not specified
Conditions
Interventions
oculomotor and pupillary function assessment
This is an exploratory open-label single-centre research project intended to collect data of PD patients in order to develop machine learning models for helping in the diagnosis and monitoring of PD. Each patient will have an initial visit and then a second visit after two weeks. The estimated duration of the study visit 1 is 3 hours, not taking into account the time for patient information and informed consent. The estimated duration of the study visit 2 is 2 hours. During these two visits, patients will undergo an MDS-UPDRS assessment, a neos examination, a standard manual ocular motor and pupil function examination, and gait assessment simultaneously with GaitQ senti and an IMU sensor placed on their back. In the two weeks separating the two visits, patient's gait will be monitored daily at home with GaitQ senti, where they will perform a daily TUG test comprising of 15 m walk, 5 sit to stand, and 5-minute walk. Healthy individuals data will be included from a previous study.
Locations (1)
University Hospital of Zurich
Zurich, Canton of Zurich, Switzerland