An Intelligent Cardiac View Classification System for Autonomous Echocardiography Robot

Abstract

The rising global prevalence of heart disease necessitates early detection for improved diagnosis and treatments. Automated echocardiography robotic systems are revolutionizing cardiology by enhancing diagnostic accuracy and efficiency. These systems integrate real-time image acquisition and processing to navigate patient anatomy and adapt imaging techniques dynamically without human intervention. Accurate cardiac view classification is vital for capturing diagnostically relevant images, forming the basis for subsequent automated disease detection and diagnosis. Although deep learning has emerged as a powerful tool for medical image analysis, its application in echocardiography remains limited due to the complexity of multi-view echocardiography imaging. The proposed system leverages deep learning models, specifically convolutional neural networks, trained on a diverse dataset of echocardiographic images to distinguish standard cardiac views, including the parasternal long-axis, parasternal short-axis, and apical four-chamber views. This capability enables the robotic system to autonomously navigate patient anatomy and optimize image acquisition in real time, minimizing operator dependency and ensuring imaging consistency. The long-term objective of this study is to develop a fully autonomous robotic system capable of early and accurate cardiovascular disease diagnosis, ultimately reducing diagnostic delays and improving patient outcomes.