A Multi-center Study on Artificial Intelligence-Based Quantitative Evaluation of Echocardiography

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

• Age ≥18 - 80 years;
• Types of diseases (8 in total, 200 cases each):

• Normal heart
• Coronary heart disease (with segmental thinning and abnormal movement)
• Valve disease (valve stenosis or reflux)
• Hypertensive heart disease
• Atrial fibrillation
• Heart failure
• Dilated cardiomyopathy
• Hypertrophic cardiomyopathy

Exclusion criteria

• Patients with congenital heart disease
• Patients with poor image quality