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

Not Applicable

Recruiting

• Conditions: Parkinson Disease; Essential Tremor; Temporomandibular Joint Disorders; Dysphonia; Dystonia; Drug Induced Dystonia; Dyskinesias; Tic Disorders; Ulnar Nerve Entrapment; Torticollis
• Interventions: Diagnostic Test: DystoniaNet-based diagnosis of isolated dystonia

• Registration Number: NCT05317390
• Lead Sponsor: Massachusetts Eye and Ear Infirmary

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.

Study Timeline

• Study First Submitted: 2021-11-28
• Study First Submitted QC: 2022-03-30
• Study First Posted: 2022-04-07
• Study Start: 2022-06-01
• Status Verified: 2024-11
• Last Update Submitted: 2024-11-19
• Last Update Posted: 2024-11-21
• Primary Completion: 2027-04-30
• Study Completion: 2027-04-30

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1000

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
Prospective clinical validation of DystoniaNet	DystoniaNet-based diagnosis of isolated dystonia	Prospective randomized studies will validate DystoniaNet performance for accurate, objective, and fast diagnosis of dystonia in the actual clinical setting.

Primary Outcome Measures

Name	Time	Method
Correctness of clinical diagnosis of dystonia using the DystoniaNet algorithm	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	4 years	The length of time (in months) from symptom onset to clinical diagnosis will be established using the DystoniaNet machine-learning algorithm

Trial Locations

Locations (1)

Massachusetts Eye and Ear Infirmary; 🇺🇸 Boston, Massachusetts, United States