Automated, vendor-agnostic measurement of myocardial tissue velocities in echocardiography

In this study, we present a vendor-agnostic, deep learning-based system for the automated analysis of transthoracic pulsed-wave tissue Doppler imaging (TDI), which decouples image acquisition from interpretation and enables centralized, fleet-wide analysis across devices. The model ingests standard TDI from heterogeneous ultrasound systems and automatically extracts key diagnostic markerspeak systolic velocity (S0), early diastolic velocity (e0), and late diastolic/atrial contraction velocity (a0) using a single, unified pipeline. Conceptually, this harmonizes measurements across vendors and sites, improving consistency, comparability, and longitudinal tracking without devicespecific calibration or tooling. Procedurally, a central inference service supports asynchronous batch processing and human-in-the-loop review, thereby shifting analysis off-console, allowing ultrasound scanners to remain fully available for acquisition. In our clinical dataset, which spans two ultrasound vendors and diverse cardiac cycles, the system correctly identified more than 93% of tissuevelocity landmarks. In 50% of studies, all automated detections matched expert annotations, eliminating the need for manual edits. This approach streamlines offline TDI analysis, accelerates turnaround, and supports scalable, standardized cardiac assessments.