DystoniaNCT05317390
Clinical Validation of DystoniaNet Deep Learning Platform for Diagnosis of Isolated Dystonia
A study testing DystoniaNet-based diagnosis of isolated dystonia in people with Dystonia and Drug Induced Dystonia, run by Massachusetts Eye and Ear Infirmary at 1 site in United States.
Trial summary
This research involves retrospective and prospective studies for clinical validation of a DystoniaNet deep learning platform for the diagnosis of isolated dystonia. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Dystonia, Drug Induced Dystonia, Parkinson Disease
- What is being tested?
- DystoniaNet-based diagnosis of isolated dystonia
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,000 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Jun 2022to 30 Apr 2028
- Phase
- Interventional
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- Males and females of diverse racial and ethnic backgrounds, with age across the lifespan;
- 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;
- 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.
Who cannot join
- Patients who are incapable of giving informed consent;
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Massachusetts Eye and Ear Infirmary
Boston, Massachusetts, 02114
Kristina Simonyan, MD, PhD, 617-573-6016, simonyan_lab@meei.harvard.edu
About this study
This research involves retrospective and prospective studies for clinical validation of a DystoniaNet deep learning platform for the diagnosis of isolated dystonia.
Read the 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
- Being tested
- DystoniaNet-based diagnosis of isolated dystonia
- Medical subject headings
- Dystonia, Parkinson Disease, Essential Tremor, Dyskinesias, Myoclonus, Tic Disorders, Torticollis, Ulnar Nerve Compression Syndromes
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
DystoniaNet-based diagnosis of isolated dystoniaDiagnostic 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
| Study group | Role | What they receive |
|---|---|---|
| Retrospective clinical validation of DystoniaNet | No intervention | 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). |
| Prospective clinical validation of DystoniaNet | Experimental | Prospective randomized studies will validate DystoniaNet performance for accurate, objective, and fast diagnosis of dystonia in the actual clinical setting. Receives: Diagnostic Test: DystoniaNet-based diagnosis of isolated dystonia |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 4 years
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
Measured: 4 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Apr 2022
- Study start1 Jun 2022
- Primary completion30 Apr 2028 (estimated)
- Study completion30 Apr 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for DystoniaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind
- Who is masked
- Participant, Care provider
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Massachusetts Eye and Ear Infirmary
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Kristina Simonyan, Massachusetts Eye and Ear Infirmary
- Investigators
- Kristina Simonyan, MD, PhD, Principal investigator, Massachusetts Eye and Ear
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05317390
- Sponsor's protocol ID
- 2020P004129
- Status last verified
- Nov 2025
- First submitted
- 28 Nov 2021
- Last updated
- 2 Dec 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No