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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT05317390
Other study ID # 2020P004129
Secondary ID
Status Recruiting
Phase N/A
First received
Last updated
Start date June 1, 2022
Est. completion date April 30, 2027

Study information

Verified date October 2023
Source Massachusetts Eye and Ear Infirmary
Contact Kristina Simonyan, MD, PhD
Phone 617-573-6016
Email simonyan_lab@meei.harvard.edu
Is FDA regulated No
Health authority
Study type Interventional

Clinical Trial Summary

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


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.


Recruitment information / eligibility

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.

Study Design


Related Conditions & MeSH terms


Intervention

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

Locations

Country Name City State
United States Massachusetts Eye and Ear Infirmary Boston Massachusetts

Sponsors (1)

Lead Sponsor Collaborator
Massachusetts Eye and Ear Infirmary

Country where clinical trial is conducted

United States, 

Outcome

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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