  Purpose: Lung disease assessment in precapillary pulmonary hypertension (PH)
is essential for appropriate patient management. This study aims to develop an
artificial intelligence (AI) deep learning model for lung texture
classification in CT Pulmonary Angiography (CTPA), and evaluate its correlation
with 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-121
classification model. "Normal", "Ground glass", "Ground glass with
reticulation", "Honeycombing", and "Emphysema" were classified as per the
Fleishner Society glossary of terms. Ground truth classes were segmented by two
radiologists with patches extracted from the labelled regions. Proportion of
lung volume for each texture was calculated by classifying patches throughout
the entire lung volume to generate a coarse texture classification mapping
throughout the lung parenchyma. AI output was assessed against diffusing
capacity of carbon monoxide (DLCO) and specialist radiologist reported disease
severity.
  Results: Micro-average AUCs for the validation, internal test, and external
test were 0.92, 0.95, and 0.94, respectively. The model had consistent
performance across parenchymal textures, demonstrated strong correlation with
diffusing capacity of carbon monoxide (DLCO), and showed good correspondence
with disease severity reported by specialist radiologists.
  Conclusion: The classification model demonstrates excellent performance on
external validation. The clinical utility of its output has been demonstrated.
This objective, repeatable measure of disease severity can aid in patient
management in adjunct to radiological reporting.
