Pulmonary hypertension in association with lung disease: quantitative CT and artificial intelligence to the rescue? state-of-the-art review
Abstract
Accurate phenotyping of patients with pulmonary hypertension (PH) is an integral part of informing disease classification, treatment, and prognosis. The impact of lung disease on PH outcomes and response to treatment remains a challenging area with limited progress. Imaging with computed tomography (CT) plays an important role in patients with suspected PH when assessing for parenchymal lung disease, however, current assessments are limited by their semi-qualitative nature. Quantitative chest-CT (QCT) allows numerical quantification of lung parenchymal disease beyond subjective visual assessment. This has facilitated advances in radiological assessment and clinical correlation of a range of lung diseases including emphysema, interstitial lung disease, and coronavirus disease 2019 (COVID-19). Artificial Intelligence approaches have the potential to facilitate rapid quantitative assessments. Benefits of cross-sectional imaging include ease and speed of scan acquisition, repeatability and the potential for novel insights beyond visual assessment alone. Potential clinical benefits include improved phenotyping and prediction of treatment response and survival. Artificial intelligence approaches also have the potential to aid more focused study of pulmonary arterial hypertension (PAH) therapies by identifying more homogeneous subgroups of patients with lung disease. This state-of-the-art review summarizes recent QCT developments and potential applications in patients with PH with a focus on lung disease.
Krit Dwivedi
PhD Student (co-sup)
Johanna Uthoff
Postdoctoral Research Associate (now at Auto Trader)
Samer Alabed
Clinical Lecturer at University of Sheffield (past PhD Student)
Haiping Lu
Professor of Machine Learning, Head of AI Research Engineering, and Turing Academic Lead
I am a Professor of Machine Learning. I develop translational AI technologies for better analysing multimodal data in healthcare and beyond.
