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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
Sponsor:
Information provided by (Responsible Party):
Kristina Simonyan, Massachusetts Eye and Ear Infirmary

Study Description
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Brief Summary:
This research involves retrospective and prospective studies for clinical validation of a DystoniaNet deep learning platform for the diagnosis of isolated dystonia.

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

Detailed Description:

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 Design
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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 1000 participants
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Double (Participant, Care Provider)
Primary Purpose: Diagnostic
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

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Dystonia

Arms and Interventions
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Arm Intervention/treatment
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


Outcome Measures
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Primary Outcome Measures :
  1. 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

  2. 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


Eligibility Criteria
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Information from the National Library of Medicine

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Ages Eligible for Study:   Child, Adult, Older Adult
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Criteria

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.
Contacts and Locations
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Information from the National Library of Medicine

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


Contacts
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Contact: Kristina Simonyan, MD, PhD 617-573-6016 simonyan_lab@meei.harvard.edu

Locations
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United States, Massachusetts
Massachusetts Eye and Ear Infirmary Recruiting
Boston, Massachusetts, United States, 02114
Contact: Kristina Simonyan, MD, PhD    617-573-6016    simonyan_lab@meei.harvard.edu   
Sponsors and Collaborators
Massachusetts Eye and Ear Infirmary
Investigators
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Principal Investigator: Kristina Simonyan, MD, PhD Massachusetts Eye and Ear
More Information
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Responsible Party: Kristina Simonyan, Associate Professor of Otolaryngology - Head and Neck Surgery, Massachusetts Eye and Ear Infirmary
ClinicalTrials.gov Identifier: NCT05317390    
Other Study ID Numbers: 2020P004129
First Posted: April 7, 2022    Key Record Dates
Last Update Posted: August 12, 2022
Last Verified: August 2022

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
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Temporomandibular Joint Disorders
Temporomandibular Joint Dysfunction Syndrome
Dysphonia
Parkinson Disease
Dystonia
Dystonic Disorders
Dyskinesias
Essential Tremor
Tic Disorders
Myoclonus
Nerve Compression Syndromes
Ulnar Nerve Compression Syndromes
Disease
Torticollis
Pathologic Processes
 Parkinsonian Disorders
Basal Ganglia Diseases
Brain Diseases
Central Nervous System Diseases
Nervous System Diseases
Movement Disorders
Synucleinopathies
Neurodegenerative Diseases
Neurologic Manifestations
Voice Disorders
Laryngeal Diseases
Respiratory Tract Diseases
Otorhinolaryngologic Diseases
Joint Diseases
Musculoskeletal Diseases