Artificial intelligence analysis of continuous wave Doppler spectra to detect reduced left ventricular ejection fraction.

Aims: To develop an artificial intelligence (AI)-driven approach that analyses 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. Current AI solutions for LVEF assessment rely on 2D imaging and ECG signals, limiting utility when data quality is poor. Methods and results: This retrospective study analysed 4231 aortic CW Doppler recordings from 3988 examinations (3580 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. 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. Conclusion: This proof-of-concept study demonstrates the feasibility of using AI to analyse 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.