Clinical Validation of DystoniaNet for Dystonia Diagnosis

This study is testing a new computer program called DystoniaNet to see how well it can diagnose dystonia (a movement disorder causing involuntary muscle contractions) and tell it apart from other conditions that have similar symptoms. Currently, it can be hard to get an accurate diagnosis for dystonia, often taking many years. Researchers want to see if DystoniaNet can provide a faster and more accurate diagnosis. The study will look at information from past patients and also follow new patients. You might be able to join if you have any form of dystonia. The main goals are to see how accurate DystoniaNet is at diagnosing dystonia and how quickly it can do so, with these results being measured over four years.

Study design
This study aims to enroll 1000 participants and involves using the DystoniaNet program for diagnosis. It includes both looking at past patient data and following new patients.
What's involved
Not specified in the trial record.
Compensation
Not stated in the trial record.
Follow-up
The main study outcomes will be measured at 4 years.

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NCT05317390

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

Recruiting
NAAll AgesInterventionalDiagnostic
Massachusetts Eye and Ear Infirmary
~1,000 participants
Updated 2025-12-02 on ClinicalTrials.gov
What's tested:DystoniaNet-based diagnosis of isolated dystonia

At a glance

Recruiting sites
1 of 1 listed site is recruiting right now
RecruitingSuspended, closed, or not yet open
What they're measuring
Correctness of clinical diagnosis of dystonia using the DystoniaNet algorithm
Measured over 4 years
+1 more outcome measured
Dystonia
Drug Induced Dystonia
Parkinson Disease
Essential Tremor
Dyskinesias
Myoclonus
Tic Disorders
Torticollis
Ulnar Nerve Entrapment
Temporomandibular Joint Disorders
Dysphonia
1 sites across 1 states
Massachusetts1
  • Kristina Simonyan, MD, PhD · PRINCIPAL_INVESTIGATOR · Massachusetts Eye and Ear

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  • Correctness of clinical diagnosis of dystonia using the DystoniaNet algorithm4 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 algorithm4 years

    The length of time (in months) from symptom onset to clinical diagnosis will be established using the DystoniaNet machine-learning algorithm