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Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidityand mortality worldwide. Identifying those at highest risk of deteriorationwould allow more effective distribution of preventative and surveillanceresources. Secondary pulmonary hypertension is a manifestation of advancedCOPD, which can be reliably diagnosed by the main Pulmonary Artery (PA) toAscending Aorta (Ao) ratio. In effect, a PA diameter to Ao diameter ratio ofgreater than 1 has been demonstrated to be a reliable marker of increasedpulmonary arterial pressure. Although clinically valuable and readilyvisualized, the manual assessment of the PA and the Ao diameters is timeconsuming and under-reported. The present study describes a non invasive methodto measure the diameters of both the Ao and the PA from contrast-enhanced chestComputed Tomography (CT). The solution applies deep learning techniques inorder to select the correct axial slice to measure, and to segment botharteries. The system achieves test Pearson correlation coefficient scores of93% for the Ao and 92% for the PA. To the best of our knowledge, it is thefirst such fully automated solution.

PHT-bot: Deep-Learning based system for automatic risk stratification of COPD patients based upon signs of Pulmonary Hypertension