Parkinson Disease Clinical Trial
Official title:
Clinical Validation of DystoniaNet Deep Learning Platform for Diagnosis of Isolated Dystonia
This research involves retrospective and prospective studies for clinical validation of a DystoniaNet deep learning platform for the diagnosis of isolated dystonia.
Status | Recruiting |
Enrollment | 1000 |
Est. completion date | April 30, 2027 |
Est. primary completion date | April 30, 2027 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | All |
Age group | N/A and older |
Eligibility | Inclusion criteria: 1. Males and females of diverse racial and ethnic backgrounds, with age across the lifespan; 2. Patients will have at least one of the forms of dystonia, including focal dystonia (e.g., laryngeal, cervical, oromandibular, blepharospasm, focal hand, musicians), segmental dystonia, or generalized dystonia; 3. Patients will have other movement disorders (Parkinson's disease, essential tremor, dyskinesia, myoclonus) and other non-neurological conditions (tic disorders, torticollis, ulnar nerve entrapments, temporomandibular disorders, dysphonia) that mimic dystonic symptoms. Exclusion criteria: 1. Patients who are incapable of giving informed consent; 2. Patients who are unable to undergo brain MRI due to the presence of certain tattoos and ferromagnetic objects in their bodies (e.g., implanted stimulators, surgical clips, prosthesis, artificial heart valve) that cannot be removed or due to pregnancy or breastfeeding at the time of the study. |
Country | Name | City | State |
---|---|---|---|
United States | Massachusetts Eye and Ear Infirmary | Boston | Massachusetts |
Lead Sponsor | Collaborator |
---|---|
Massachusetts Eye and Ear Infirmary |
United States,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Correctness of clinical diagnosis of dystonia using the DystoniaNet algorithm | Correctness of dystonia diagnosis (yes dystonia/no dystonia) will be established using the DystoniaNet machine-learning algorithm | 4 years | |
Primary | Time of clinical diagnosis of dystonia using the DystoniaNet algorithm | The length of time (in months) from symptom onset to clinical diagnosis will be established using the DystoniaNet machine-learning algorithm | 4 years |
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