Pediatric Epilepsy
Conditions
Brief summary
A diagnostic accuracy study on Artificial intelligence EEG analysis system assisted doctors to diagnose pediatric epilepsy.
Interventions
DIAGNOSTIC_TESTAI EEG analysis system
The first stage, physicians independently diagnose pediatric epilepsy with EEG; the second stage, Artificial intelligence EEG analysis system assisted physicians to diagnose pediatric epilepsy
Sponsors
Kunming Children's Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
No minimum to 18 Years
Healthy volunteers
Yes
Inclusion criteria
* Age \< 18 years old * Children with suspected epilepsy
Exclusion criteria
* 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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| To evaluate the coincidence rate between doctors' independent diagnosis and the diagnosis recommended by the AI system. | Within 48 hours after the completion of EEG monitoring | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| To evaluate the diagnostic efficiency of clinicians at two-stage | Immediately after the end of EEG interpretation | The time taken by physicians to interpret EEG independently and with the aid of AI was measured. |
Outcome results
None listed