Clinical Validation of DystoniaNet Deep Learning Platform for Diagnosis of Isolated Dystonia
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|ClinicalTrials.gov Identifier: NCT05317390|
Recruitment Status : Recruiting
First Posted : April 7, 2022
Last Update Posted : August 12, 2022
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|Condition or disease||Intervention/treatment||Phase|
|Dystonia Drug Induced Dystonia Parkinson Disease Essential Tremor Dyskinesias Myoclonus Tic Disorders Torticollis Ulnar Nerve Entrapment Temporomandibular Joint Disorders Dysphonia||Diagnostic Test: DystoniaNet-based diagnosis of isolated dystonia||Not Applicable|
Isolated dystonia is a movement disorder of unknown pathophysiology, which causes involuntary muscle contractions leading to abnormal, typically patterned, twisting movements and postures. A significant challenge in the clinical management of dystonia is due to the absence of a biomarker and associated 'gold' standard diagnostic test. Currently, the diagnosis of dystonia is guided by clinical evaluations of its symptoms, which lead to a low agreement between clinicians and a high rate of diagnostic inaccuracies. It is estimated that only 5% of patients receive an accurate diagnosis at symptom onset, and the average diagnostic delay extends up to 10.1 years. This study will conduct retrospective and prospective studies to clinically validate the performance of DystoniaNet, a biomarker-based deep learning platform for the diagnosis of isolated dystonia.
The retrospective studies will clinically validate the diagnostic performance of the DystoniaNet algorithm (1) in patients compared to healthy subjects (normative test), and (2) between patients with dystonia and other neurological and non-neurological conditions (differential test).
The prospective randomized study will validate the performance of DystoniaNet algorithm for accurate, objective, and fast diagnosis of dystonia in the actual clinical setting.
This research is expected to advance the DystoniaNet algorithm for dystonia diagnosis into its clinical use for increased accuracy of dystonia diagnosis. Early detection and diagnosis of dystonia will enable its early therapy and improved prognosis, having an overall positive impact on healthcare and patients' quality of life.
|Study Type :||Interventional (Clinical Trial)|
|Estimated Enrollment :||1000 participants|
|Intervention Model:||Parallel Assignment|
|Masking:||Double (Participant, Care Provider)|
|Official Title:||Clinical Validation of DystoniaNet Deep Learning Platform for Diagnosis of Isolated Dystonia|
|Actual Study Start Date :||June 1, 2022|
|Estimated Primary Completion Date :||April 30, 2027|
|Estimated Study Completion Date :||April 30, 2027|
No Intervention: Retrospective clinical validation of DystoniaNet
Retrospective studies will (1) clinically validate the diagnostic performance of DystoniaNet compared to a normal neurological state (normative test), and (2) develop and test DystoniaNet extensions in comparison with other neurological and non-neurological conditions (differential test).
Experimental: Prospective clinical validation of DystoniaNet
Prospective randomized studies will validate DystoniaNet performance for accurate, objective, and fast diagnosis of dystonia in the actual clinical setting.
Diagnostic Test: DystoniaNet-based diagnosis of isolated dystonia
DystoniaNet 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 [ Time Frame: 4 years ]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 [ Time Frame: 4 years ]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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|Ages Eligible for Study:||Child, Adult, Older Adult|
|Sexes Eligible for Study:||All|
|Accepts Healthy Volunteers:||Yes|
- 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.
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT05317390
|Contact: Kristina Simonyan, MD, PhDfirstname.lastname@example.org|
|United States, Massachusetts|
|Massachusetts Eye and Ear Infirmary||Recruiting|
|Boston, Massachusetts, United States, 02114|
|Contact: Kristina Simonyan, MD, PhD 617-573-6016 email@example.com|
|Principal Investigator:||Kristina Simonyan, MD, PhD||Massachusetts Eye and Ear|
|Responsible Party:||Kristina Simonyan, Associate Professor of Otolaryngology - Head and Neck Surgery, Massachusetts Eye and Ear Infirmary|
|Other Study ID Numbers:||
|First Posted:||April 7, 2022 Key Record Dates|
|Last Update Posted:||August 12, 2022|
|Last Verified:||August 2022|
|Studies a U.S. FDA-regulated Drug Product:||No|
|Studies a U.S. FDA-regulated Device Product:||No|
Temporomandibular Joint Disorders
Temporomandibular Joint Dysfunction Syndrome
Nerve Compression Syndromes
Ulnar Nerve Compression Syndromes
Basal Ganglia Diseases
Central Nervous System Diseases
Nervous System Diseases
Respiratory Tract Diseases