Articles in Press, Accepted Manuscript Available Online from 25 November 2025
Manual interpretation of echocardiography is a labor-intensive process characterized by significant inter-observer variability. Although Deep Learning (DL) has shown expert-level potential, its clinical integration is often hindered by "domain shift" across different ultrasound vendors and a lack of multi-view analysis capabilities. To address these challenges, we developed EchoAI, a secure, vendor-agnostic web-based Clinical Decision Support System (CDSS) that incorporates a multi-task Unsupervised Domain Adaptation (UDA) engine. This platform enables the simultaneous and automated quantification of Left Ventricular Ejection Fraction (LVEF) and cardiac wall thickness across standard A4C, A2C, and PLAX views. EchoAI utilizes a user-centered design and a "human-in-the-loop" workflow, allowing physicians to verify and edit AI-generated segmentation masks in real-time, thereby fostering diagnostic trust. Clinical validation conducted at Guilan University of Medical Sciences (GUMS) demonstrated high operational efficiency with an average processing time of 1.15 seconds per cardiac cycle. The system achieved a strong correlation with expert manual measurements (r=0.95, P<0.001) and an exceptionally low mean bias of -0.17%. Usability assessments yielded a high satisfaction score of 6.18 out of 7, with 86% of the AI outputs being accepted without modification. By providing an interactive and transparent interface, EchoAI effectively bridges the gap between algorithmic potential and routine clinical practice, offering a scalable solution for enhanced cardiac assessment in diverse healthcare settings.