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

Neonatal Seizure Clinical Trial

Official title:

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

Clinical Trial Details — Status: Withdrawn

Administrative data

• NCT number: NCT04991779
• Other study ID #: CHFudanU_NNICU16
• Status: Withdrawn
• Start date: March 16, 2022
• Est. completion date: May 16, 2022

Study information

• Verified date: December 2023
• Source: Children's Hospital of Fudan University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

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.

Recruitment information / eligibility

• Status: Withdrawn
• Enrollment: 0
• Est. completion date: May 16, 2022
• Est. primary completion date: May 16, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 0 Days to 28 Days

Eligibility

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.

Related Conditions & MeSH terms

• Neonatal Seizure
• Seizures

Intervention

Diagnostic Test:
• AI-assisted cEEG detection tool
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.

Locations

Country	Name	City	State
China	Chengdu Women's and Children's Central Hospital	Chengdu	Sichuan
China	Children Hospital of Fudan University	Shanghai	Shanghai
China	Henan Children's Hospital	Zhengzhou	Henan

Sponsors (4)

Lead Sponsor	Collaborator
Children's Hospital of Fudan University	Chengdu Women's and Children's Central Hospital, Kunming Children's Hospital, Xiamen Children's Hospital

Country where clinical trial is conducted

China, 

References & Publications (3)

Hoodbhoy Z, Masroor Jeelani S, Aziz A, Habib MI, Iqbal B, Akmal W, Siddiqui K, Hasan B, Leeflang M, Das JK. Machine Learning for Child and Adolescent Health: A Systematic Review. Pediatrics. 2021 Jan;147(1):e2020011833. doi: 10.1542/peds.2020-011833. Epub — View Citation

Rennie JM, de Vries LS, Blennow M, Foran A, Shah DK, Livingstone V, van Huffelen AC, Mathieson SR, Pavlidis E, Weeke LC, Toet MC, Finder M, Pinnamaneni RM, Murray DM, Ryan AC, Marnane WP, Boylan GB. Characterisation of neonatal seizures and their treatmen — View Citation

Shellhaas RA, Chang T, Tsuchida T, Scher MS, Riviello JJ, Abend NS, Nguyen S, Wusthoff CJ, Clancy RR. The American Clinical Neurophysiology Society's Guideline on Continuous Electroencephalography Monitoring in Neonates. J Clin Neurophysiol. 2011 Dec;28(6 — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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