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
- Are there results?
- Not yet: the study is under way
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
Study at a glance
From the registry record, last updated by the sponsor on 2 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinical Validation of DystoniaNet Deep Learning Platform for Diagnosis of Isolated Dystonia
- ClinicalTrials.gov ID
- NCT05317390
- Acronym / sponsor ID
- 2020P004129
- Conditions
- Dystonia, Drug Induced Dystonia, Parkinson Disease, Essential Tremor, Dyskinesias, Myoclonus
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Massachusetts Eye and Ear InfirmaryUniversity, hospital or other
- Study start
- 1 Jun 2022 (actual)
- Primary completion
- 30 Apr 2028 (estimated)
- Study completion
- 30 Apr 2028 (estimated)
- First posted
- 7 Apr 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (participant, care provider)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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 | Diagnostic Test: DystoniaNet-based diagnosis of isolated dystoniaProspective randomized studies will validate DystoniaNet performance for accurate, objective, and fast diagnosis of dystonia in the actual clinical setting. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (2 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| DystoniaNet-based diagnosis of isolated dystoniaDystoniaNet will be used for the diagnosis of dystonia and its differential diagnosis from other neurological and non-neurological disorders mimicking symptoms of dystonia | Diagnostic test | Prospective clinical validation of DystoniaNet |
Primary outcome
Correctness of clinical diagnosis of dystonia using the DystoniaNet algorithm
Time frame: 4 years
Correctness of dystonia diagnosis (yes dystonia/no dystonia) will be established using the DystoniaNet machine-learning algorithm
Time of clinical diagnosis of dystonia using the DystoniaNet algorithm
Time frame: 4 years
The length of time (in months) from symptom onset to clinical diagnosis will be established using the DystoniaNet machine-learning algorithm
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Dec 2025.
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Focused Ultrasound Unilateral Pallidotomy for Medication-Refractory Limb Dystonia
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Deep Brain Stimulation Surgery for Movement Disorders
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Kristina Simonyan, MD, PhD principal investigator
Massachusetts Eye and Ear
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
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.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 2 Dec 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Kristina Simonyan, MD, PhD (617-573-6016, simonyan_lab@meei.harvard.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Correctness of clinical diagnosis of dystonia using the DystoniaNet algorithm (time frame: 4 years). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Dec 2025.