Artificial Intelligence-based Video Analysis to Detect Infantile Spasms

A Machine Learning Approach to Infantile Spasms Recognition in Video Recordings

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06315829

Enrollment

Registered

2024-03-18

Start date

2024-08-26

Completion date

2026-05-06

Last updated

2026-05-07

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Infantile Spasms

Keywords

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

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.

Interventions

DEVICESpasm Vision

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

Study design

Observational model

OTHER

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

No minimum to 2 Years

Healthy volunteers

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

Design outcomes

Primary

Measure	Time frame	Description
Model Sensitivity (Recall)	2 years	Proportion of true positives which the model classified correctly in the test dataset.
Model Specificity	2 years	Proportion of true negatives which the model classified correctly in the test dataset.
Model Positive Predictive Value (Precision)	2 years	Proportion of positive classifications which were correct in the test dataset.
Model Negative Predictive Value	2 years	Proportion of negative classifications which were correct in the test dataset.

Countries

United States

Contacts

PRINCIPAL_INVESTIGATOREric Kossoff, MD

Johns Hopkins Neurology

PRINCIPAL_INVESTIGATORRama Chellappa, PhD

Johns Hopkins Biomedical Engineering

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: May 8, 2026