AI-assisted cEEG Diagnosis of Neonatal Seizures in Neonatal Intensive Care Unit

Fudan University

Status: Withdrawn

Conditions: Neonatal Seizure

Treatments: Diagnostic Test: AI-assisted cEEG detection tool

Study type: Observational

Funder types: Other

Identifiers

NCT04991779

CHFudanU_NNICU16

Details and patient eligibility

About

A diagnostic accuracy study on Artificial intelligence assisted continue EEG diagnostic tool is to carried out comparing with manually EEG interpretation as the golden standard for neonatal seizure.

Full description

The occurrence of neonatal seizures may be the first, and perhaps the only, clinical sign of a central nervous system disorder in the newborn infant. The incidence of neonatal seizures is variable based on gestational age. The etiology of seizures may indicate the presence of a potentially treatable etiology and should prompt an immediate evaluation to determine the cause and to initiate etiology-specific therapy. Importantly, the earlier treatment of seizures positively affects the infant's long-term neurological development. However, even when continue electroencephalogram (cEEG) monitoring is available, the availability of on-site expertise to interpret cEEG signals is limited and in practice, the diagnosis is still based only on clinical signs. The previous study indicated that the reliable seizure detection was as little as 10% of seizure events. Therefore, an early automated seizure detection tool has been developed based on machine learning. The lack of an automated seizure detection tool has been validated in the external neonatal seizures cohort. The evidence on the utility of the automated seizure detection tool remains uncertain. This is a prospective, continuous double-blind designed diagnostic accuracy study. The study aims to validate the accuracy of the artificial intelligence (AI)-assisted cEEG diagnostic tool comparing the manually cEEG interpretation as the golden standard of neonatal seizure in neonatal intensive care units. Analysis of sensitivity and specificity is to evaluate the accuracy of AI-assisted cEEG diagnostic tool.

Sex: All

Ages: Under 28 days old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Postnatal age < or = 28 days;
• cEEG monitoring at least 12hours monitoring;
• Suspected seizures;
• Risk of Intracranial hemorrhage;
• Abnormality of MRI or ultrasound before cEEG;
• Neonates diagnosed with encephalopathy or suspected of encephalopathy;
• Hypoxic-ischemic encephalopathy or suspected hypoxic-ischemic encephalopathy;
• Metabolic disturbances (Hypoglycemia, Hypocalcemia, Hypomagnesemia, Inborn errors of metabolism);
• Central nervous system (CNS) or systemic infections;
• Postsurgical neonatal within 3 days;
• Suspected genetic disease or Positive genetic diagnoses;

Exclusion criteria

• The neonates with head scalp defect, scalp hematoma, edema and other contraindications which are not suitable for cEEG monitoring during hospitalization.

Trial design

0 participants in 1 patient group

The neonates with suspected seizures or high risk of seizures

Description:

The neonates with suspected seizures or high risk of seizures are monitored by continuous electroencephalogram (cEEG) at least 12 hours since admission. The cEEG will be interpreted by AI-assisted cEEG diagnostic tool at the end of cEEG monitoring. At the same time, the same cEEG will be manually reported according the reference standard.

Treatment:

Diagnostic Test: AI-assisted cEEG detection tool