Artificial Intelligence in Routine Non-contrast CT Imaging to Assess Cardiothoracic Structures and Evaluate Clinical Utility

Background:
Pulmonary hypertension (PH) is a progressive and life-threatening condition characterised by elevated pulmonary arterial pressure and associated with different diseases, including left heart and lung diseases. Early diagnosis is essential to improve clinical outcomes; however, current diagnostic pathways rely on invasive right heart catheterisation or contrast-enhanced imaging, which are not always feasible in routine clinical practice and are associated with different complications. Non-contrast chest computed tomography (CT) is widely available and frequently performed in patients with PH, many of whom also exhibit coronary artery calcification. However, its full diagnostic and prognostic potentials remain underexplored. Although several manually derived CT measurements have been proposed as imaging predictors, their clinical utility is limited by inter-observer variability and time-consuming analysis.
Aim:
This thesis investigates the use of artificial intelligence (AI)–driven volumetric measurements of multi-cardiothoracic structures from non-gated, non-contrast CT to enhance both the diagnosis and prognostic assessment of PH and coronary artery calcification.
Methods:
A multi-cardiothoracic structure and coronary artery calcification AI-based segmentation models were developed at the University of Sheffield using internal and external cohorts from the ASPIRE registry. The models were benchmarked against the gold standard haemodynamic, reference standards, visual assessments, and validated tool (e.g. TotalSegmentator).
Results:
The developed AI-based models demonstrate high diagnostic accuracy in predicting PH and detecting coronary artery calcifications. The models were comparable to TotalSegmentator and demonstrated higher accuracy than manual clinical practice techniques when evaluated in exploratory testing. AI-derived right atrial volume and coronary artery calcifications are independently associated with increased mortality in PH patients, even after adjustment for age, sex, PH subgroups, and REVEAL score.
Conclusion:
Automated segmentation and volumetric measurements of multi-cardiothoracic structures in non-contrast CT have the potential to facilitate earlier and accurate diagnosis and prognostic assessment of coronary artery calcification and PH patients, including lung and left heart diseases.