Deep-learning networks accurately detect pulmonary hypertension in patients with idiopathic pulmonary fibrosis

There is currently no widely used noninvasive tool that accurately predicts pulmonary hypertension (PH) in patients with idiopathic pulmonary fibrosis (IPF). We aimed to assess the feasibility of developing deep-learning algorithms for detecting PH in patients with IPF.

A retrospective derivation cohort included patients with IPF and PH and patients with IPF without PH, matched for age, sex and forced vital capacity (FVC%). Individual echocardiographic frames were split into a training and a validation set. The network consisted of three connected two-dimensional convolutional subnetworks; each one accepted an apical four-chamber view, a parasternal short-axis view and a lung computed tomography view from the same individual, respectively. The output was then merged with serum brain natriuretic peptide levels and diffusion lung capacity of carbon monoxide.

Overall, 65 patients with PH and IPF (68.6±7.2 years, 75.4% female, FVC 62.7%) and 65 matched patients without PH were included in the derivation cohort. A training set of 122 patients with and without PH revealed a >99% accuracy in detecting PH. A validation set demonstrated 87.5% accuracy per patient for correctly detecting PH (area under the curve (AUC) 0.971, 95% CI 00.521-1.000; p=0.0004; sensitivity 83.3%, specificity 100%). An external testing set of six patients with IPF and PH and six matched patients without PH showed 87.5% accuracy per patient (AUC 0.844, 95% CI 0.540-0.992; p=0.03; sensitivity 100%, specificity 75.0%).

Deep-learning algorithms can accurately predict PH in patients with IPF. Further validation in a larger external cohort is warranted.