Single View Echocardiographic Analysis for Left Ventricular Outflow Tract Obstruction Prediction in Hypertrophic Cardiomyopathy: A Deep Learning Approach

Background: Accurate left ventricular outflow tract obstruction (LVOTO) assessment is crucial for hypertrophic cardiomyopathy (HCM) management and prognosis. Traditional methods often require multiple echocardiographic views, Doppler imaging, and provocation maneuvers, which may not always be feasible or readily available. Objective: To develop and validate a deep learning (DL) model capable of predicting severe LVOTO in HCM patients using only the parasternal long-axis (PLAX) view from transthoracic echocardiography (TTE). Methods: A DL model was trained on PLAX videos extracted from TTE examinations (developmental dataset, n=1,007) to capture both morphological and dynamic motion features, generating a DL index for LVOTO (DLi-LVOTO, range 0-100). Performance was evaluated in an internal test dataset (ITDS, n=87) and externally validated in the distinct hospital dataset (DHDS, n=1,334) and the LVOTO reduction treatment dataset (n=156). Results: The model achieved high accuracy in detecting severe LVOTO, with area under the receiver operating characteristics curve (AUROC) of 0.97 (95% confidence interval: 0.92-1.00) in ITDS and 0.93 (0.92-0.95) in DHDS. At a DLi-LVOTO threshold of 70, the model demonstrated a specificity of 97.3% and negative predictive value (NPV) of 96.1% in ITDS. In DHDS, a cutoff of 60 yielded a specificity of 94.6% and NPV of 95.5%. DLi-LVOTO also decreased significantly after surgical myectomy or Mavacamten treatment, correlating with reductions in peak pressure gradient (p<0.001 for all). Conclusions: Our DL-based approach predicts severe LVOTO using only the PLAX view from TTE, serving as a complementary tool when conventional Doppler evaluation is limited and for monitoring treatment response.