A Clinical Trial With a Self-controlled, Multicenter, Pediatric EEG Intelligent Analysis System to Assist in Diagnosis

NCT06918457 | Not Yet Recruiting

• 1 other identifier: 2024-03-278-K01
• Study Type: observational
• Target: 1,000
• Locations: 0 countries
• Sites: N/A

Brief Summary

A diagnostic accuracy study on Artificial intelligence EEG analysis system assisted doctors to diagnose pediatric epilepsy.

Conditions

Pediatric Epilepsy

Outcome Measures

Primary Outcomes (1)

• To evaluate the coincidence rate between doctors' independent diagnosis and the diagnosis recommended by the AI system.

  The reference standard is the EGG interpreted by 3 clinicians who had attended the uniformly training program and had more than 5 years of experience in diagnosing epilepsy in children.

  Within 48 hours after the completion of EEG monitoring

Secondary Outcomes (1)

• To evaluate the diagnostic efficiency of clinicians at two-stage

  Immediately after the end of EEG interpretation

Interventions

AI EEG analysis system DIAGNOSTIC_TEST

The first stage, physicians independently diagnose pediatric epilepsy with EEG; the second stage, Artificial intelligence EEG analysis system assisted physicians to diagnose pediatric epilepsy

Eligibility Criteria

• Age: Up to 18 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

children aged \< 18 years old with suspected epilepsy who need EEG examination

You may qualify if:

• Age \< 18 years old
• Children with suspected epilepsy

You may not qualify if:

• During EEG monitoring, the patients had other serious neurological diseases and mental diseases concurrently
• Used medication that affect EEG data within 3 weeks, such as sedatives and anti-epileptic medications
• Substandard data quality, such as data lack of key records, electrode connection discontinuity, insufficient recording time, or the presence of serious artifacts
• Incomplete or missing data
• Equipment or operational abnormalities, data for which EEG monitoring has not been performed continuously

Sponsors & Collaborators

• Kunming Children's Hospital: lead

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: March 27, 2025
• First Posted: April 9, 2025
• Study Start: June 3, 2025
• Primary Completion (Estimated): June 1, 2026
• Study Completion (Estimated): July 1, 2026
• Last Updated: April 9, 2025

Record last verified: 2025-03

Data Sharing

• IPD Sharing: Will not share