Clinical trial NCT05317390: Clinical Validation of DystoniaNet Deep Learning Platform for Diagnosis of Isolated Dystonia
Official title: Clinical Validation of DystoniaNet Deep Learning Platform for Diagnosis of Isolated Dystonia
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Dystonia
Drug Induced Dystonia
Parkinson Disease
Essential Tremor
Dyskinesias
Myoclonus
Tic Disorders
Torticollis
Ulnar Nerve Entrapment
Temporomandibular Joint Disorders
Dysphonia
Lead sponsor: Massachusetts Eye and Ear Infirmary
Sponsor class: OTHER
First posted: 2022-04-07
Enrollment: 1000
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: DIAGNOSTIC

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.

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.

Primary outcomes:
measure: Correctness of clinical diagnosis of dystonia using the DystoniaNet algorithm
timeFrame: 4 years
measure: Time of clinical diagnosis of dystonia using the DystoniaNet algorithm
timeFrame: 4 years
Secondary outcomes:
Other outcomes:
Locations:
city: Boston
country: United States
facility: Massachusetts Eye and Ear Infirmary
state: Massachusetts
Study officials:
affiliation: Massachusetts Eye and Ear
name: Kristina Simonyan, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References: