Artificial Intelligence-based Video Analysis to Detect Infantile Spasms
A Machine Learning Approach to Infantile Spasms Recognition in Video Recordings
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Actual)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Glenn Rivera, MD
- Phone Number: 410-955-4259
- Email: griver14@jh.edu
Study Locations
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Maryland
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Baltimore, Maryland, United States, 21287
- Johns Hopkins Hospital
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-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Participant age less than 24 months
- Participant evaluated in the Johns Hopkins Outpatient Center, Johns Hopkins Pediatric Emergency Department or Johns Hopkins Inpatient Units due to spells of abnormal movement or seizure
- Participant evaluated by a pediatric neurologist during the outpatient or inpatient visit at Johns Hopkins Hospital
- At least one video recording of the spell of abnormal movement produced by the parent/guardian available for provider review
Exclusion Criteria:
- Poor video recording quality
- Entire patient is not in frame
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Confirmed Epileptic Spasms (Positive Class)
Participants diagnosed with infantile spasms based upon historical data and supportive electroencephalography data (i.e.
hypsarrhythmia or modified hypsarrhythmia background).
|
Machine learning software developed to analyze videos and accurately distinguish infantile spasms from visually similar movements.
|
|
Epileptic Spasm Mimics (Negative Class)
Participants diagnosed with non-epileptic movements (e.g.
Sandifer syndrome, shuddering attacks, stretching, stereotypy, startle reflex, writhing movements, jitteriness, sleep myoclonus) based upon historical data and supportive electroencephalography data (when available).
|
Machine learning software developed to analyze videos and accurately distinguish infantile spasms from visually similar movements.
|
|
Awake and Alert (Negative Class)
Participants exhibiting spontaneous, subtle movements in the awake and alert state.
|
Machine learning software developed to analyze videos and accurately distinguish infantile spasms from visually similar movements.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Model Sensitivity (Recall)
Time Frame: 2 years
|
Proportion of true positives which the model classified correctly in the test dataset.
|
2 years
|
|
Model Specificity
Time Frame: 2 years
|
Proportion of true negatives which the model classified correctly in the test dataset.
|
2 years
|
|
Model Positive Predictive Value (Precision)
Time Frame: 2 years
|
Proportion of positive classifications which were correct in the test dataset.
|
2 years
|
|
Model Negative Predictive Value
Time Frame: 2 years
|
Proportion of negative classifications which were correct in the test dataset.
|
2 years
|
Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Eric Kossoff, MD, Johns Hopkins Neurology
- Principal Investigator: Rama Chellappa, PhD, Johns Hopkins Biomedical Engineering
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- IRB00429753
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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