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A Deep Learning Framework for Pediatric TLE Detection Using 18F-FDG-PET Imaging

NCT04169581 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 201

Last updated 2020-01-02

No results posted yet for this study

Summary

This study aims to use radiomics analysis and deep learning approaches for seizure focus detection in pediatric patients with temporal lobe epilepsy (TLE). Ten positron emission tomograph (PET) radiomics features related to pediatric temporal bole epilepsy are extracted and modelled, and the Siamese network is trained to automatically locate epileptogenic zones for assistance of diagnosis.

Conditions

  • Epilepsy, Temporal Lobe

Sponsors & Collaborators

  • Second Affiliated Hospital, School of Medicine, Zhejiang University

    lead OTHER

Eligibility

Min Age
6 Years
Max Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2018-06-01
Primary Completion
2019-02-28
Completion
2019-04-30

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04169581 on ClinicalTrials.gov