Artificial Intelligence-based Video Analysis to Detect Infantile Spasms

NCT ID: NCT06315829

Last Updated: 2025-08-19

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

200 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-08-26

Study Completion Date

2026-05-31

Brief Summary

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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.

Detailed Description

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Conditions

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Infantile Spasms

Study Design

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Observational Model Type

OTHER

Study Time Perspective

PROSPECTIVE

Study Groups

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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).

Spasm Vision

Intervention Type DEVICE

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).

Spasm Vision

Intervention Type DEVICE

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.

Spasm Vision

Intervention Type DEVICE

Machine learning software developed to analyze videos and accurately distinguish infantile spasms from visually similar movements.

Interventions

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Spasm Vision

Machine learning software developed to analyze videos and accurately distinguish infantile spasms from visually similar movements.

Intervention Type DEVICE

Eligibility Criteria

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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
Maximum Eligible Age

2 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Johns Hopkins University

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Eric Kossoff, MD

Role: PRINCIPAL_INVESTIGATOR

Johns Hopkins Neurology

Rama Chellappa, PhD

Role: PRINCIPAL_INVESTIGATOR

Johns Hopkins Biomedical Engineering

Locations

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Johns Hopkins Hospital

Baltimore, Maryland, United States

Site Status RECRUITING

Countries

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United States

Central Contacts

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Glenn Rivera, MD

Role: CONTACT

410-955-4259

Facility Contacts

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Glenn Rivera, MD

Role: primary

410-955-4259

Other Identifiers

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IRB00429753

Identifier Type: -

Identifier Source: org_study_id

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