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Prediction of Stroke Risk in Patients with Atrial Fibrillation Based on Chest CT Images

NCT06611995 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 1500

Last updated 2024-09-25

No results posted yet for this study

Summary

This study aims to create and assess a deep learning framework for extracting left atrial appendage features in atrial fibrillation patients and combining them with clinical data to predict ischemic stroke risk. Clinical data and chest CT images from patients diagnosed with non-valvular atrial fibrillation will be collected. Patients will be categorized into stroke and non-stroke groups to build a data repository. The dataset will be divided into training and validation sets, with missing data handled and pulmonary vein CTV and virtual non-contrast images annotated. A deep learning model will be used for image segmentation and feature extraction to develop a prediction system.

Conditions

Interventions

OTHER

observational study

Observational study without intervention

Sponsors & Collaborators

  • The Fourth Affiliated Hospital of Zhejiang University School of Medicine

    collaborator OTHER

  • Taizhou Hospital

    collaborator OTHER

  • Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University

    collaborator OTHER

  • Hangzhou Hospital of Traditional Chinese Medicine

    collaborator OTHER

  • Ningbo Medical Center Lihuili Hospital

    collaborator OTHER_GOV

  • Sanmen People Hospital

    collaborator UNKNOWN

  • Zhejiang Rongjun Hospital

    collaborator OTHER_GOV

  • First Affiliated Hospital of Zhejiang University

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-09-23
Primary Completion
2026-09-30
Completion
2026-09-30

Countries

  • China

Study Locations

More Related Trials

Entities

Diseases

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 NCT06611995 on ClinicalTrials.gov