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Purpose: Lung disease assessment in precapillary pulmonary hypertension (PH)is essential for appropriate patient management. This study aims to develop anartificial intelligence (AI) deep learning model for lung textureclassification in CT Pulmonary Angiography (CTPA), and evaluate its correlationwith clinical assessment methods.  Materials and Methods: In this retrospective study with external validation,122 patients with pre-capillary PH were used to train (n=83), validate (n=17)and test (n=10 internal test, n=12 external test) a patch based DenseNet-121classification model. "Normal", "Ground glass", "Ground glass withreticulation", "Honeycombing", and "Emphysema" were classified as per theFleishner Society glossary of terms. Ground truth classes were segmented by tworadiologists with patches extracted from the labelled regions. Proportion oflung volume for each texture was calculated by classifying patches throughoutthe entire lung volume to generate a coarse texture classification mappingthroughout the lung parenchyma. AI output was assessed against diffusingcapacity of carbon monoxide (DLCO) and specialist radiologist reported diseaseseverity.  Results: Micro-average AUCs for the validation, internal test, and externaltest were 0.92, 0.95, and 0.94, respectively. The model had consistentperformance across parenchymal textures, demonstrated strong correlation withdiffusing capacity of carbon monoxide (DLCO), and showed good correspondencewith disease severity reported by specialist radiologists.  Conclusion: The classification model demonstrates excellent performance onexternal validation. The clinical utility of its output has been demonstrated.This objective, repeatable measure of disease severity can aid in patientmanagement in adjunct to radiological reporting.

Deep learning automated quantification of lung disease in pulmonary  hypertension on CT pulmonary angiography: A preliminary clinical study with  external validation