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

Parkinson Disease Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05317390
• Other study ID #: 2020P004129
• Status: Recruiting
• Phase: N/A
• 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
• 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.

Related Conditions & MeSH terms

• Dyskinesias
• Dysphonia
• Dystonia
• Dystonic Disorders
• Essential Tremor
• Myoclonus
• Nerve Compression Syndromes
• Parkinson Disease
• Temporomandibular Joint Disorders
• Temporomandibular Joint Dysfunction Syndrome
• Tic Disorders
• Torticollis
• Ulnar Nerve Entrapment

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