A framework of deep learning networks provides expert-level accuracy for the detection and prognostication of pulmonary arterial hypertension

Aims: To test the hypothesis that deep learning (DL) networks reliably detect pulmonary arterial hypertension (PAH) and provide prognostic information.

Methods and results: Consecutive patients with PAH, right ventricular (RV) dilation (without PAH), and normal controls were included. An ensemble of deep convolutional networks incorporating echocardiographic views and estimated RV systolic pressure (RVSP) was trained to detect (invasively confirmed) PAH. In addition, DL-networks were trained to segment cardiac chambers and extracted geometric information throughout the cardiac cycle. The ability of DL parameters to predict all-cause mortality was assessed using Cox-proportional hazard analyses. Overall, 450 PAH patients, 308 patients with RV dilatation (201 with tetralogy of Fallot and 107 with atrial septal defects) and 67 normal controls were included. The DL algorithm achieved an accuracy and sensitivity of detecting PAH on a per patient basis of 97.6 and 100%, respectively. On univariable analysis, automatically determined right atrial area, RV area, RV fractional area change, RV inflow diameter and left ventricular eccentricity index (P < 0.001 for all) were significantly related to mortality. On multivariable analysis DL-based RV fractional area change (P < 0.001) and right atrial area (P = 0.003) emerged as independent predictors of outcome. Statistically, DL parameters were non-inferior to measures obtained manually by expert echocardiographers in predicting prognosis.

Conclusion: The study highlights the utility of DL algorithms in detecting PAH on routine echocardiograms irrespective of RV dilatation. The algorithms outperform conventional echocardiographic evaluation and provide prognostic information at expert-level. Therefore, DL methods may allow for improved screening and optimized management of PAH.