Health TrialsbyHealthPanel

Development of Digital Diagnostic Devices for Parkinson's Disease

ConditionParkinson Disease
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
AgeNot listed
SponsormachineMD AG

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

Diagnosis of Parkinson's disease or of another parkinsonian syndrome (atypical Parkinson's)

Refractive error between -6 and +4 diopters, on both eyes

Informed consent by participant documented per signature

Able to self-report history of daily gait freezing and/or festination

Disqualifiers

Other known neurological diseases

Current medication/drugs that could potentially influence performance in ocular motor tasks and/or compliance in the judgement of the investigator (e.g. benzodiazepines, alcohol, stimulants, or recreational drugs) - except Parkinson's medications

Incapacity to understand and comply with the examination (e.g. due to advanced cognitive decline, failure to comply with easy experimental instructions and tasks)

Any injury or disorder that may affect eye movement measurements or balance (other than Parkinson's or referring primary condition)

Trial population

At least 100 patients with typical PD and at least 12 patients with atypical Parkinson's.

Trial design

Design model

Other

Time perspective

Prospective

Treatments tested in this trial

  • oculomotor and pupillary function assessment

    Diagnostic test

    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.

Treatment groups

100 Participants
are divided into 2 treatment groups
Group A: people with Parkinson's disease1 intervention
Group B: Atypical parkinsonism1 intervention

Trial outcomes

Primary outcomes

1

Development of machine learning models for diagnosing and monitoring of PD

The machine learning algorithms will be trained based on a clinical dataset of 50 PD patients, healthy individuals (data from another study), and 12 additional patients with other parkinsonian disorders. This dataset consists of ocular motor and pupil data provided by neos, ocular motor and pupil assessment provided by the standard clinical examination, gait data provided by GaitQ senti (placed on the patient's leg), gait data provided by an IMU sensor placed on the patient's back, demographic information (age, sex, ethnicity, eye colour), clinical information (disease stage, disease duration, age of onset of disease, medication, MDS-UPDRS score).

Time frame
1 year

Secondary outcomes

1

Correlation with clinical parameters

The secondary outcome is correlation between ocular motor parameters with clinical parameters (disease stage, disease duration, age of onset of disease, medication, MDS-UPDRS score).

Time frame
1 year

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

machineMD AG

Lead sponsor

University of Zurich

Collaborator

University Hospital, Zürich

Collaborator

University of Exeter

Collaborator

gaitQ Limited

Collaborator

Source ClinicalTrials.gov
Apply now