This project aims to collaborate with multiple medical institutions to verify the accuracy, stability, and clinical application value of AI algorithms in echocardiographic quantitative measurement through multi-center clinical research. Specific objectives include:

1. Compare the automatic measurement results of AI with the manual measurement data from physicians of different levels, and analyze the measurement deviation and consistency of AI in key parameters such as intracardiac diameter, volume, and function.
2. Investigate whether AI-assisted measurement can significantly reduce echocardiogram analysis time and optimize clinical workflows. Through multi-center data validation, establish a standardized reference system for AI ultrasound measurement, promote the promotion and application of AI technology in medical institutions at all levels, and reduce diagnostic differences between different hospitals and physicians.
3. Exploring the application of AI in special cases: Assessing the measurement stability of AI algorithms in complex cases (such as cardiomyopathy, valvular disease, coronary heart disease, etc.), and optimizing AI models to meet broader clinical needs.

Cardiovascular disease is a major threat to the health of Chinese residents, and echocardiography, as its core diagnostic tool, directly affects clinical decision-making in terms of measurement accuracy and efficiency. However, traditional ultrasound evaluation heavily relies on physician experience, with pain points such as strong subjectivity, time-consuming measurements, and uneven levels of primary diagnosis. There is an urgent need for technological innovation to improve diagnostic standardization. In recent years, artificial intelligence (AI) technology has shown great potential in the field of medical image analysis, which can achieve automated quantitative measurement of cardiac chamber structure and function. However, existing AI models generally have problems such as insufficient multi center validation and limited adaptability to complex cases, which restrict their clinical translation and application.

To overcome these bottlenecks, this project collaborates with multiple medical institutions to conduct clinical research, systematically evaluating the measurement differences between AI algorithms and physicians of different levels, and assessing the accuracy and stability of AI algorithms. The research will focus on verifying the value of AI technology in improving diagnostic consistency, optimizing workflows, and exploring its potential applications in complex cardiovascular diseases. By establishing a standardized evaluation system, this project aims to promote the standardized application of AI ultrasound technology, ultimately achieving the goal of improving diagnosis and treatment efficiency, promoting the sinking of high-quality medical resources, and helping to improve the overall level of cardiovascular disease prevention and treatment.

Inclusion Criteria:

1. Age ≥18 - 80 years;

2. Types of diseases (8 in total, 200 cases each):

   1. Normal heart
   2. Coronary heart disease (with segmental thinning and abnormal movement)
   3. Valve disease (valve stenosis or reflux)
   4. Hypertensive heart disease
   5. Atrial fibrillation
   6. Heart failure
   7. Dilated cardiomyopathy
   8. Hypertrophic cardiomyopathy

Exclusion Criteria:

1. Patients with congenital heart disease
2. Patients with poor image quality

Patients who require cardiac ultrasound examination for various reasons in clinical practice. Ultrasound and clinical diagnosis of patients with normal cardiac structure and function, as well as those with coronary heart disease, valve disease, hypertensive heart disease, atrial fibrillation, heart failure, dilated cardiomyopathy, and hypertrophic cardiomyopathy. A total of 1600 adults were enrolled in multiple centers;