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

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

Official Title
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A Multicenter Diagnostic Study on the Utility of AI-assisted Continue EEG Diagnosis of Neonatal Seizures

Conditions
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Neonatal Seizure

Intervention / Treatment
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* Diagnostic Test: AI-assisted cEEG detection tool


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 0 Days
Maximum Age: 28 Days

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| The neonates with suspected seizures or high risk of seizures<br>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. | Diagnostic Test: AI-assisted cEEG detection tool<br>* This study is an observational study to evaluate the accuracy of AI-assisted cEEG diagnostic tool with routine care. All patients from the cohort accept cEEG monitoring and AI-assisted cEEG detection tool. The tool included a quantitive EEG neural signal processing pipeline to extract features from the original signal datasets, machine learning models based on gradient boosted model for prediction. The reference standard is the electrographic seizures interpreted by 3 clinicians who had attended the uniformly training program and were certified by the Chinese Anti-Epilepsy Association.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The accuracy of AI-assisted cEEG diagnostic tool in evaluating the neonatal seizure | The accuracy of includes sensitivity and specificity. The reference standard is the electrographic seizures interpreted by 3 clinicians who had attended the uniformly training program and were certified by the Chinese Anti-Epilepsy Association. Sensitivity is defined as: The proportion of neonates with seizures is successfully screened out by AI-assisted cEEG diagnostic tool. Specificity is defined as: The proportion of neonates without seizures who are not recognized as seizures by AI-assisted cEEG diagnostic tool. | within 7 days since the end of cEEG monitoring during hospitalization |