Ignite Creation Date:
2024-10-26 @ 3:41 PM
Last Modification Date:
2024-10-26 @ 3:41 PM
Study NCT ID:
NCT06611995
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-09-18
Brief Title:
Prediction of Stroke Risk in Patients with Atrial Fibrillation Based on Chest CT Images
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Prediction of Stroke Risk in Patients with Atrial Fibrillation Based on Chest CT Images
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 patients39 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None