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

NCT ID: NCT04991779

Last Updated: 2023-12-29

Basic Information

• Recruitment Status: WITHDRAWN
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-03-16
• Study Completion Date: 2022-05-16

Brief 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.

Detailed 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.

Conditions

Neonatal Seizure

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

The neonates with suspected seizures or high risk of seizures

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.

AI-assisted cEEG detection tool

Intervention Type: DIAGNOSTIC_TEST

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.

Interventions

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.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

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.

• Minimum Eligible Age: 0 Days
• Maximum Eligible Age: 28 Days
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Chengdu Women's and Children's Central Hospital

OTHER

Sponsor Role: collaborator

Xiamen Children's Hospital

OTHER

Sponsor Role: collaborator

Kunming Children's Hospital

OTHER

Sponsor Role: collaborator

Children's Hospital of Fudan University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Wenhao Zhou, Ph.D

Role: STUDY_CHAIR

Children's Hospital of Fudan University

Locations

Henan Children's Hospital

Zhengzhou, Henan, China

Children Hospital of Fudan University

Shanghai, Shanghai Municipality, China

Chengdu Women's and Children's Central Hospital

Chengdu, Sichuan, China

Countries

China

References

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 treatment using continuous EEG monitoring: a multicentre experience. Arch Dis Child Fetal Neonatal Ed. 2019 Sep;104(5):F493-F501. doi: 10.1136/archdischild-2018-315624. Epub 2018 Nov 24.

Reference Type: RESULT

PMID: 30472660 (View on PubMed)

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):611-7. doi: 10.1097/WNP.0b013e31823e96d7. No abstract available.

Reference Type: RESULT

PMID: 22146359 (View on PubMed)

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 2020 Dec 15.

Reference Type: RESULT

PMID: 33323492 (View on PubMed)

Other Identifiers

CHFudanU_NNICU16

Identifier Type: -

Identifier Source: org_study_id