DystoniaNet Diagnosis for Dystonia

Overseen By Kristina Simonyan, MD, PhD

Age: Any Age

Sex: Any

Trial Phase: Academic

Sponsor: Massachusetts Eye and Ear Infirmary

Disqualifiers: Inability to consent, MRI restrictions, pregnancy, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

1000 Participants Needed

Trial Summary

Do I need to stop my current medications for the trial?

The trial information does not specify whether you need to stop taking your current medications.

What data supports the effectiveness of the DystoniaNet treatment for diagnosing dystonia?

The DystoniaNet deep learning platform has shown a high accuracy of 98.8% in diagnosing dystonia by identifying specific brain regions associated with the condition. This is a significant improvement over the current 34% agreement between clinicians, suggesting that DystoniaNet can enhance clinical decision-making by providing a more reliable diagnosis. ^{1 2 3 4 5}

How does the DystoniaNet treatment differ from other treatments for dystonia?

DystoniaNet is unique because it uses a deep learning platform to diagnose dystonia by identifying a specific biomarker in brain MRIs, achieving high accuracy and reducing misdiagnosis, unlike traditional methods that rely heavily on clinician experience and often result in diagnostic delays. ^{1 2 3 5 6}

What is the purpose of this trial?

This trial aims to validate a computer program called DystoniaNet that helps doctors diagnose dystonia more accurately. It targets patients with isolated dystonia who often experience delays in diagnosis. The program uses artificial intelligence to learn from data and identify signs of the disorder, improving diagnosis speed and accuracy.

Research Team

Kristina Simonyan, MD, PhD

Principal Investigator

Massachusetts Eye and Ear

Eligibility Criteria

This trial is for individuals with various forms of dystonia or conditions that resemble dystonic symptoms, such as Parkinson's disease and essential tremor. It includes people of all ages, genders, and ethnic backgrounds. Those who can't give consent or have MRI-incompatible body modifications or devices are excluded.

Inclusion Criteria

I have a type of dystonia, such as in my neck, eyes, jaw, hand, or it affects my whole body.

I have a type of dystonia, such as in my neck, eyes, jaw, hand, or it's more widespread.

I have a condition like Parkinson's, essential tremor, or another similar disorder.

Exclusion Criteria

You cannot have an MRI of your brain because you have certain tattoos or metal objects inside your body that cannot be removed, or because you are pregnant or breastfeeding.

I am unable to understand and give consent for my treatment.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Retrospective Study

Retrospective studies will clinically validate the diagnostic performance of DystoniaNet compared to a normal neurological state and other conditions.

4 years

Prospective Study

Prospective randomized studies will validate DystoniaNet performance for accurate, objective, and fast diagnosis of dystonia in the clinical setting.

4 years

Follow-up

Participants are monitored for safety and effectiveness after diagnosis using the DystoniaNet algorithm.

4 weeks

Treatment Details

Interventions

• DystoniaNet

Trial Overview The study is testing DystoniaNet, a deep learning platform designed to diagnose isolated dystonia by analyzing medical data. The research will look back at past cases and also include new patients to validate the accuracy of this diagnostic tool.

Participant Groups

Group I: Prospective clinical validation of DystoniaNet Experimental Treatment 1 Intervention

Prospective randomized studies will validate DystoniaNet performance for accurate, objective, and fast diagnosis of dystonia in the actual clinical setting.

Group II: Retrospective clinical validation of DystoniaNet Active Control 1 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).

Who Is Running the Clinical Trial?

Massachusetts Eye and Ear Infirmary

Lead Sponsor

Trials

115

Recruited

15,000+

Findings from Research

A deep learning algorithm called DystoniaNet was developed to diagnose isolated dystonia from brain MRIs, achieving an impressive accuracy of 98.8% across 612 subjects, including 392 patients with different forms of dystonia and 220 healthy controls.

DystoniaNet identified specific brain regions as biomarkers for dystonia, significantly improving diagnostic agreement among clinicians from 34% to a much higher level, thus enhancing clinical decision-making and potentially reducing misdiagnosis and delays in treatment.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A microstructural neural network biomarker for dystonia diagnosis identified by a DystoniaNet deep learning platform. Valeriani, D., Simonyan, K.[2021]

A study of 1,618 participants with isolated non-focal dystonia used data-driven clustering to identify common patterns of dystonia affecting various body regions, revealing strong associations between bilateral upper and lower limbs, which may indicate a new subtype of dystonia.

The analysis highlighted three major clusters of dystonia: cervical dystonia with nearby regions, bilateral hand dystonia, and cranial dystonia, reinforcing the idea of segmental patterns in dystonia affecting the cranial and cervical areas.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Anatomical categorization of isolated non-focal dystonia: novel and existing patterns using a data-driven approach. Younce, JR., Cascella, RH., Berman, BD., et al.[2023]

A deep learning model was able to accurately classify individual features of hyperkinetic seizures, achieving an F1 score of 0.84 for detecting emotional signs and 0.83 for identifying dystonia, based on a dataset of 38 seizure videos from 19 patients.

This study demonstrates the potential of using advanced deep learning techniques to automate the analysis of seizure characteristics, which could enhance the understanding and treatment of epilepsy.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Automated video analysis of emotion and dystonia in epileptic seizures. Hou, JC., Thonnat, M., Bartolomei, F., et al.[2022]

This study demonstrates that using smartphone-coupled inertial sensors and machine learning can provide objective, home-based assessments of dystonia severity in children with dyskinetic cerebral palsy, achieving average F1 scores of 0.67 for lower and 0.68 for upper extremities.

The results suggest that these automated assessments could complement traditional clinical evaluations, offering more frequent and reliable monitoring of dystonia, although further research is needed to improve model accuracy and data collection.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Home-Based Measurements of Dystonia in Cerebral Palsy Using Smartphone-Coupled Inertial Sensor Technology and Machine Learning: A Proof-of-Concept Study. den Hartog, D., van der Krogt, MM., van der Burg, S., et al.[2022]

A new neural network-based software has been developed to objectively measure and recognize symptoms of blepharospasm (BSP), including blinks and spasms, which can help standardize the assessment of this complex condition.

The software demonstrated high sensitivity for detecting brief and prolonged spasms, making it a promising tool for clinicians to accurately evaluate BSP severity compared to traditional methods.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A neural network-based software to recognise blepharospasm symptoms and to measure eye closure time. Trotta, GF., Pellicciari, R., Boccaccio, A., et al.[2020]

Dystonia is a complex movement disorder characterized by involuntary, repetitive movements and abnormal postures, which can be challenging to diagnose due to its similarity across various causes.

The review proposes an eight-question diagnostic approach to help clinicians, both specialists and general neurologists, effectively identify dystonic syndromes and determine appropriate investigations for accurate diagnosis.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Diagnosis of dystonic syndromes--a new eight-question approach. Bertram, KL., Williams, DR.[2022]

References

1. United States pubmed.ncbi.nlm.nih.gov

A microstructural neural network biomarker for dystonia diagnosis identified by a DystoniaNet deep learning platform. [2021]

2. Switzerland pubmed.ncbi.nlm.nih.gov

Anatomical categorization of isolated non-focal dystonia: novel and existing patterns using a data-driven approach. [2023]

3. Netherlands pubmed.ncbi.nlm.nih.gov

Automated video analysis of emotion and dystonia in epileptic seizures. [2022]

4. Switzerland pubmed.ncbi.nlm.nih.gov

Home-Based Measurements of Dystonia in Cerebral Palsy Using Smartphone-Coupled Inertial Sensor Technology and Machine Learning: A Proof-of-Concept Study. [2022]

5. United States pubmed.ncbi.nlm.nih.gov

A neural network-based software to recognise blepharospasm symptoms and to measure eye closure time. [2020]

6. England pubmed.ncbi.nlm.nih.gov

Diagnosis of dystonic syndromes--a new eight-question approach. [2022]