Artificial Intelligence Analysis of Continuous Wave Doppler Spectra to Detect Reduced Left Ventricular Ejection Fraction

Abstract Objectives To develop an artificial intelligence (AI)-driven approach that analyzes continuous-wave (CW) Doppler spectra from the aortic valve (AV) to detect reduced (≤40%) left ventricular ejection fraction (LVEF) without requiring dedicated two-dimensional left ventricular imaging or ECG-gated volumetric analysis for model inference. Background Current AI solutions for LVEF assessment rely on 2D imaging and ECG signals, limiting utility when data quality is poor. Methods This retrospective study analyzed 4,231 aortic CW Doppler recordings from 3,988 examinations (3,580 patients). Preprocessing yielded 13,359 single-peak images. A CoAtNet-2 neural network was developed using patient-level training and validation cohorts and evaluated on an independent held-out test cohort. The network generated predictions for each single-peak image, and these probabilities were then averaged per examination. Maximum AV blood flow velocity and average CW Doppler pixel intensity were measured. Results LVEF ≤40% occurred in 20.8% of examinations, associating with lower maximum AV velocity and higher average pixel intensity. In the independent test cohort (782 examinations), the model achieved 85.2% accuracy, 79.0% sensitivity, 86.7% specificity, an AUC of 0.906, an NPV of 94.3%, and a PPV of 59.9%, with robust performance across subgroups. Conclusions This proof-of-concept study demonstrates the feasibility of using AI to analyze CW Doppler spectra for rapid, non-invasive identification of reduced LVEF without requiring dedicated two-dimensional left ventricular imaging or ECG-gated volumetric analysis for model inference. By leveraging underutilized echocardiographic Doppler data, this signal-based approach may serve as an adjunctive screening or rule-out tool, particularly when standard imaging or ECG-gated analysis is limited or delayed.