Artificial Intelligence-based Video Analysis to Detect Infantile Spasms

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.

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Key information

Conditions

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

Age range

Up to 2 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Johns Hopkins Hospital

Baltimore, Maryland, 21287, United States

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Spasm Vision

Device

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

Primary outcomes

Model Sensitivity (Recall)

Time frame: 2 years

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

Time frame: 2 years

Proportion of true negatives which the model classified correctly in the test dataset.
Model Positive Predictive Value (Precision)

Time frame: 2 years

Proportion of positive classifications which were correct in the test dataset.
Model Negative Predictive Value

Time frame: 2 years

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

Sponsors and collaborators

Lead sponsor

Johns Hopkins University

Other

Registry information

Official study title

A Machine Learning Approach to Infantile Spasms Recognition in Video Recordings

Important dates

Study start: 2024
Primary completion: 2026
Study completion: 2026
First posted: Mar 18, 2024
Registry last updated: May 7, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed