Automated Echocardiographic Detection of Coronary Artery Disease Using Artificial Intelligence Methods
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Coronary Artery Disease. Basic parameters: 18 years — 90 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
The incidence rate and mortality of coronary artery disease are increasing year by year. Exploring non-invasive, accurate, and widely applicable methods to screen and diagnosis is of great significance. New ultrasound techniques, such as non-invasive myocardial work, have been proven to be superior to traditional ultrasound techniques in screening and diagnosis. However, diagnostic analysis based on ultrasound video images is time-consuming and subjective. The progress of artificial intelligence technology in fully automated quantitative evaluation of video images provides the possibility for computer-aided design screening and diagnosis. At present, the application of artificial intelligence in computer-aided design is a cutting-edge issue in the field of cardiovascular disease research. The application of artificial intelligence technology in the construction of computer-aided diagnostic models based on ultrasound video images is still in its early stages.
Detailed description
1\) Clarify the value of new cardiac ultrasound techniques indicators in coronary artery disease diagnosis; 2) To achieve classification and detection of cardiac ultrasound sections; Implementing automatic segmentation and recognition of the left ventricular cavity, left ventricular myocardium, and left atrial wall contours through the CLAS model; Using the another model to achieve heart motion tracking and synthesizing velocity vector maps of the heart flow field. 3) Verify and optimize the coronary artery disease fully automated artificial intelligence diagnostic model mentioned above.
Primary outcome measures
- Different coronary angiography results [Time frame: Coronary angiography examination within 2-3 days after admission]
Eligibility criteria
Inclusion criteria
- Patients with suspected coronary artery disease
- Patients plan to undergo coronary angiography
Exclusion criteria
- Patients with aortic valve stenosis
- Patients with aortic valve replacement surgery
- Patients with hypertrophic cardiomyopathy
- Patients with severe heart valve disease
- Patients with severe arrhythmia
- Patients with severe cardiomyopathy
- Patients with severe congenital heart disease
- The quality of ultrasound images is poor
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Study design
- Observational model
- Other
Study locations
China · 1 center
- Beijing Hospital — Beijing
Identifiers
NCT: NCT06314295 · BeijingH-WF