This research involves retrospective and prospective studies for clinical validation of a DystoniaNet deep learning platform for the diagnosis of isolated dystonia.
Males and females of diverse racial and ethnic backgrounds, with age across the lifespan;
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;
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.
Patients who are incapable of giving informed consent;
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.
Parallel
DystoniaNet-based diagnosis of isolated dystonia
Diagnostic testDystoniaNet will be used for the diagnosis of dystonia and its differential diagnosis from other neurological and non-neurological disorders mimicking symptoms of dystonia
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
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
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