Background

In infants, pulmonary hypertension (PH) increases morbidity and mortality. Echocardiography, though standard, is time- and expertise-demanding. We propose a deep learning approach for automated PH detection using standard echocardiography videos, validated by the systolic eccentricity index (EIs).
Methods

The training and validation set comprised 975 videos and the held-out set 378 videos, including five echocardiographic standard views from infants aged 3–90 days, taken between 2018–2021 and 2021–2022, respectively. Echocardiograms were labeled as PH (EIs < 0.82) and healthy (EIs ≥ 0.87). After preprocessing and random segmentation of all videos into 13.530 frames, spatial and spatio-temporal convolutional neural network architectures were used for training of a PH prediction model and gradient-weighted class activation mapping for explainability.
Results

The best single-view performance was achieved using parasternal short axis view (AUROC spatial and spatio-temporal: 0.91 and 0.94 in validation set, 0.93 and 0.88 in held-out set, respectively). Combination of three standard views improved accuracy with AUROC 0.96 and 0.90 in validation (spatio-temporal) and held-out set (spatial), respectively. Saliency maps revealed model focus on clinically relevant regions, including interventricular septum and left atrial filling.
Conclusions

The presented deep learning model for automated detection of PH in neonates shows high accuracy, explainability, and reproducibility.