A Comprehensive System Design and Implementation of AI Powered Beat - To – Beat Stroke Volume Estimation Using Doppler Echocardiography

Accurate and continuous monitoring of cardiac stroke volume is critical for assessing cardiovascular health, particularly in intensive care units and during surgical procedures. Traditional methods of stroke volume estimation using Doppler echocardiography rely heavily on manual measurements, which are time-consuming, operator-dependent, and unsuitable for real-time monitoring. This research presents an AI-powered, fully automated system for beat-to-beat stroke volume estimation using Doppler echocardiography. The system leverages advanced image processing techniques, deep learning models (including U-Net for segmentation), and signal analysis algorithms to automatically detect individual heartbeats, trace Doppler waveforms, calculate the Velocity-Time Integral (VTI), measure the Left Ventricular Outflow Tract (LVOT) area, and compute stroke volume for each cardiac cycle. The proposed system eliminates manual intervention, reduces measurement errors, and enables continuous, real-time cardiac monitoring. Validation against expert annotations demonstrates an accuracy of ≥95%, with processing times suitable for clinical deployment. This system has significant potential to improve patient outcomes in critical care settings, providing clinicians with rapid, reliable, and actionable cardiac performance data.