Ignite Creation Date:
2024-05-06 @ 7:35 PM
Last Modification Date:
2024-10-26 @ 3:09 PM
Study NCT ID:
NCT06062316
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-04-30
First Post:
2023-09-17
Brief Title:
Warning Model of Myocardial Remodeling After Acute Myocardial Infarction Using Multimodal Feature Structure Technology
Sponsor:
Xuanwu Hospital Beijing
Organization:
Xuanwu Hospital Beijing
Study Overview
Official Title:
Early Warning Model of Myocardial Remodeling After Acute Myocardial Infarction Based on Multimodal Feature Structure Technology
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2023-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Acute myocardial infarction AMI is one of the most important diseases threatening human life The existing MI prognosis prediction scales mostly predict the incidence of death recurrent MI and heart failure through 6-8 clinical text indicators and the data are collected relatively simply Myocardial remodeling as an adverse pathological change that can start and continue to progress in the early stage after myocardial infarction is the main pathological mechanism of heart failure and death However there is no quantitative early-warning model of myocardial remodeling and the clinical guidance of early intervention is lacking
Our previous study found that cardiac magnetic resonance imaging can accurately quantify the necrotic area and recoverable myocardium in the edematous myocardium after myocardial infarction In this study machine learning algorithm variable convolution network DCN and capsule network capsnet are used to build a new neural network architecture Structural feature extraction of multi-modal clinical image data such as MRI and ultrasound is realized Combined with the established database of 3000 patients with myocardial infarction the multimodal feature matrix will be constructed and a variety of classifiers such as support vector machine SVM and random forest RF will be used for quantitative prediction of myocardial remodeling and the effects of different classifiers were evaluated It is expected that this project will establish a quantitative early warning model of myocardial remodeling after acute myocardial infarction in line with the characteristics of Chinese people The same type of data outside the database will be used for verification to establish an efficient and stable early warning model
Our previous study found that cardiac magnetic resonance imaging can accurately quantify the necrotic area and recoverable myocardium in the edematous myocardium after myocardial infarction In this study machine learning algorithm variable convolution network DCN and capsule network capsnet are used to build a new neural network architecture Structural feature extraction of multi-modal clinical image data such as MRI and ultrasound is realized Combined with the established database of 3000 patients with myocardial infarction the multimodal feature matrix will be constructed and a variety of classifiers such as support vector machine SVM and random forest RF will be used for quantitative prediction of myocardial remodeling and the effects of different classifiers were evaluated It is expected that this project will establish a quantitative early warning model of myocardial remodeling after acute myocardial infarction in line with the characteristics of Chinese people The same type of data outside the database will be used for verification to establish an efficient and stable early warning model
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None