Three-Dimensional Echocardiographic Automated Quantification of Left Heart Chamber Volumes Using an Adaptive...

Although 3D echocardiography (3DE) allows accurate and reproducible quantification of cardiac chambers, it has not been integrated into clinical practice because it relies on manual input, which interferes with workflow. A recently developed automated adaptive analytics algorithm for simultaneous quantification of left ventricular and atrial (LV, LA) volumes was found to be accurate and reproducible in patients with good images. We sought to prospectively test its feasibility and accuracy in consecutive patients in relationship with image quality and reader experience.

Three hundred consecutive patients underwent 3DE. Image quality was graded as poor, adequate, or good. Images were analyzed by an expert echocardiographer to obtain LV volumes and ejection fraction (EF) and LA volume using the automated analysis (HeartModel, Philips, Andover, MA) with and without editing the endocardial boundaries and using conventional manual tracing (QLAB, Philips, Andover, MA) blinded to the automated measurements as a reference. In a subgroup of 100 patients, automated analysis was repeated by two readers without 3DE experience.

Automated analysis failed in 31/300 patients (10%). Patients with poor image quality (n = 72, 24%) showed suboptimal agreement with the reference technique, especially for LVEF. Importantly, patients with adequate (n = 89, 30%) and good (n = 108, 36%) images showed small biases and excellent correlations without border corrections, which were further improved with editing. In contrast, border corrections by inexperienced readers did not improve the agreement with reference values.

Automated 3DE analysis allows accurate quantification of left-heart size and function in 66% of consecutive patients, while in the remaining patients, its performance is limited/unreliable due to image quality. Border corrections require 3DE experience to improve the accuracy of the automated measurements. In patients with sufficient image quality, this automated approach has the potential to overcome the workflow limitations of the 3D analysis in clinical practice.