Artificial Intelligence-based Video Analysis to Detect Infantile Spasms

Infantile Spasms Clinical Trial

Official title:

A Machine Learning Approach to Infantile Spasms Recognition in Video Recordings

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06315829
• Other study ID #: IRB00429753
• Status: Not yet recruiting
• Start date: May 2024
• Est. completion date: May 2026

Study information

• Verified date: March 2024
• Source: Johns Hopkins University
• Contact: Glenn Rivera, MD
• Phone: 410-955-4259
• Email: griver14[@]jh.edu
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 200
• Est. completion date: May 2026
• Est. primary completion date: May 2026
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A to 2 Years

Eligibility

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

Related Conditions & MeSH terms

• Infantile Spasms
• Muscle Cramp
• Spasm
• Spasms, Infantile

Intervention

Device:
• Spasm Vision
Machine learning software developed to analyze videos and accurately distinguish infantile spasms from visually similar movements.

Locations

Country	Name	City	State
United States	Johns Hopkins Hospital	Baltimore	Maryland

Sponsors (1)

Lead Sponsor	Collaborator
Johns Hopkins University

Country where clinical trial is conducted

United States, 

Outcome

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