Artificial Intelligence-based Video Analysis to Detect Infantile Spasms

NCT06315829 | Recruiting

• 1 other identifier: IRB00429753
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Infantile Spasms

Keywords

Seizure; Epilepsy; Infantile Spasm; Epileptic Spasm; West Syndrome; Hypsarrhythmia; Artificial Intelligence; Video Analysis; Computer Vision; Machine Learning

Outcome Measures

Primary Outcomes (4)

• Model Sensitivity (Recall)

  Proportion of true positives which the model classified correctly in the test dataset.

  2 years

• Model Specificity

  Proportion of true negatives which the model classified correctly in the test dataset.

  2 years

• Model Positive Predictive Value (Precision)

  Proportion of positive classifications which were correct in the test dataset.

  2 years

• Model Negative Predictive Value

  Proportion of negative classifications which were correct in the test dataset.

  2 years

Study Arms (3)

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).

Device: Spasm Vision

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).

Device: Spasm Vision

Awake and Alert (Negative Class)

Participants exhibiting spontaneous, subtle movements in the awake and alert state.

Device: Spasm Vision

Interventions

Spasm Vision DEVICE

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

Awake and Alert (Negative Class); Confirmed Epileptic Spasms (Positive Class); Epileptic Spasm Mimics (Negative Class)

Eligibility Criteria

• Age: Up to 2 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17)
• Sampling Method: Non-Probability Sample

Study Population

Participants will be recruited from the outpatient and inpatient settings of Johns Hopkins Hospital, an academic medical center located in Baltimore, Maryland offering tertiary and quaternary care.

You may qualify if:

• 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

You may not qualify if:

• Poor video recording quality
• Entire patient is not in frame

Sponsors & Collaborators

• Johns Hopkins University: lead

Study Sites (1)

Johns Hopkins Hospital

Baltimore, Maryland, 21287, United States

RECRUITING

MeSH Terms

Conditions

Spasms, Infantile; Seizures; Epilepsy

Condition Hierarchy (Ancestors)

Epilepsy, Generalized; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Epileptic Syndromes; Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Study Officials

• Eric Kossoff, MD

  Johns Hopkins Neurology

  PRINCIPAL INVESTIGATOR

• Rama Chellappa, PhD

  Johns Hopkins Biomedical Engineering

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Glenn Rivera, MD

CONTACT

410-955-4259; griver14@jh.edu

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 11, 2024
• First Posted: March 18, 2024
• Study Start: August 26, 2024
• Primary Completion: May 1, 2026
• Study Completion: May 1, 2026
• Last Updated: August 19, 2025

Record last verified: 2025-08

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)