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

NCT ID: NCT05317390

Last Updated: 2025-12-02

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 1000 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2022-06-01
• Study Completion Date: 2028-04-30

Brief Summary

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

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.

Conditions

Dystonia; Drug Induced Dystonia; Parkinson Disease; Essential Tremor; Dyskinesias; Myoclonus; Tic Disorders; Torticollis; Ulnar Nerve Entrapment; Temporomandibular Joint Disorders; Dysphonia

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: DOUBLE

Study Groups

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).

Group Type: NO_INTERVENTION

No interventions assigned to this group

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.

Group Type: EXPERIMENTAL

DystoniaNet-based diagnosis of isolated dystonia

Intervention Type: DIAGNOSTIC_TEST

DystoniaNet will be used for the diagnosis of dystonia and its differential diagnosis from other neurological and non-neurological disorders mimicking symptoms of dystonia

Interventions

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

Intervention Type: DIAGNOSTIC_TEST

Eligibility 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.

• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Massachusetts Eye and Ear Infirmary

OTHER

Sponsor Role: lead

Responsible Party

Kristina Simonyan

Professor of Otolaryngology - Head and Neck Surgery

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Kristina Simonyan, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Massachusetts Eye and Ear

Locations

Massachusetts Eye and Ear Infirmary

Boston, Massachusetts, United States

Site Status: RECRUITING

Countries

United States

Central Contacts

Kristina Simonyan, MD, PhD

Role: CONTACT

simonyan_lab@meei.harvard.edu

617-573-6016

Facility Contacts

Kristina Simonyan, MD, PhD

Role: primary

simonyan_lab@meei.harvard.edu

617-573-6016

Other Identifiers

2020P004129

Identifier Type: -

Identifier Source: org_study_id