Atrial Fibrillation (AF), Ischemic Stroke
Conditions
Keywords
Atrial fibrillation, Ischemic Stroke, Deep Learning, Chest CT, Left Atrial Appendage
Brief 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.
Detailed description
This study aims to develop and evaluate a deep learning framework that can automatically extract imaging features of the left atrial appendage in patients with atrial fibrillation and combine them with clinical features to predict the risk of ischemic stroke in these patients. The study intends to retrospectively collect clinical data (including patients' general information, medical history, laboratory tests, etc.) and chest CT images, as well as pulmonary vein CTV images (if available), from patients diagnosed with non-valvular atrial fibrillation between January 2018 and June 2024. The patients will be divided into stroke and non-stroke groups based on whether they have experienced an ischemic stroke, and a data analysis repository will be established. The dataset will be split into training and validation sets. Missing data will be handled, and data labeling will be performed on the pulmonary vein CTV sequence images and virtual non-contrast (VNC) sequence images. The left atrial morphology will be delineated, and a deep learning-based image segmentation network model will be developed to extract and select radiomic features for the prediction system.
Interventions
OTHERobservational study
Observational study without intervention
Sponsors
The Fourth Affiliated Hospital of Zhejiang University School of Medicine
Taizhou Hospital
Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University
Hangzhou Hospital of Traditional Chinese Medicine
Ningbo Medical Center Lihuili Hospital
Sanmen People Hospital
Zhejiang Rongjun Hospital
First Affiliated Hospital of Zhejiang University
Study design
Observational model
CASE_CONTROL
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
Diagnosed with atrial fibrillation by ECG, 24-hour Holter monitor, or recordable ECG monitor; atrial fibrillation confirmed by an implanted pacemaker or defibrillator, lasting at least 30 seconds Available chest CT images and complete clinical data.
Exclusion criteria
Incomplete clinical data or diagnosis of valvular atrial fibrillation (e.g., rheumatic heart valve disease, post-valve replacement) Poor-quality CT images that prevent complete assessment of left atrial appendage morphology Patients who have undergone left atrial appendage closure Patients who have had radiofrequency ablation or cardioversion with no evidence of recurrence post-procedure
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Performance of a Deep Learning Framework for Predicting Ischemic Stroke Risk in AF Patients. | Through study completion, an average of 2 year. | This study aims to develop and evaluate a deep learning framework that automatically extracts Left Atrial Appendage (LAA) imaging features from 3D\_slicer software and combines them with clinical characteristics to predict ischemic stroke risk in patients with atrial fibrillation (AF). The performance of the developed system will be evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), accuracy, sensitivity, and specificity. |
Countries
China
Contacts
Primary ContactXiaosheng Hu
Outcome results
None listed