Clinical Validation of DystoniaNet Deep Learning Platform for Diagnosis of Isolated Dystonia

Last updated: November 19, 2024
Sponsor: Massachusetts Eye and Ear Infirmary
Overall Status: Active - Recruiting

Phase

N/A

Condition

Dyskinesias

Essential Tremor

Oral Facial Pain

Treatment

DystoniaNet-based diagnosis of isolated dystonia

Clinical Study ID

NCT05317390
2020P004129
  • All Genders
  • Accepts Healthy Volunteers

Study Summary

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

Eligibility Criteria

Inclusion

Inclusion criteria:

  1. Males and females of diverse racial and ethnic backgrounds, with age across thelifespan;

  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) thatmimic dystonic symptoms.

Exclusion

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 tattoosand ferromagnetic objects in their bodies (e.g., implanted stimulators, surgicalclips, prosthesis, artificial heart valve) that cannot be removed or due topregnancy or breastfeeding at the time of the study.

Study Design

Total Participants: 1000
Treatment Group(s): 1
Primary Treatment: DystoniaNet-based diagnosis of isolated dystonia
Phase:
Study Start date:
June 01, 2022
Estimated Completion Date:
April 30, 2027

Study 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.

Connect with a study center

  • Massachusetts Eye and Ear Infirmary

    Boston, Massachusetts 02114
    United States

    Active - Recruiting

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