Artificial Intelligence-based Video Analysis to Detect Infantile Spasms
A Machine Learning Approach to Infantile Spasms Recognition in Video Recordings
Johns Hopkins University
200 participants
Aug 26, 2024
OBSERVATIONAL
Conditions
Summary
Infantile spasms are a type of seizure linked to developmental issues. Unfortunately, they are often misdiagnosed, causing delays in treatment. The purpose of this study is to develop a computer program that can reliably differentiate infantile spasms from similar, yet benign movements in videos. This computer program will learn from videos taken by parents of study participants. Quickly recognizing and treating infantile spasms is crucial for ensuring the best developmental outcomes.
Eligibility
Inclusion Criteria4
- 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 Criteria2
- Poor video recording quality
- Entire patient is not in frame
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Interventions
Machine learning software developed to analyze videos and accurately distinguish infantile spasms from visually similar movements.
Locations(1)
View Full Details on ClinicalTrials.gov
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NCT06315829