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

## TL;DR

This paper presents PHT-bot, a deep learning system that automatically measures pulmonary artery and aorta diameters from CT scans to identify COPD patients at risk of pulmonary hypertension, improving diagnostic efficiency.

## Contribution

It introduces the first fully automated deep learning method for measuring artery diameters from CT scans to assess pulmonary hypertension risk in COPD patients.

## Key findings

- Achieved 93% correlation for aorta measurements
- Achieved 92% correlation for pulmonary artery measurements
- First fully automated solution for this measurement task

## Abstract

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality worldwide. Identifying those at highest risk of deterioration would allow more effective distribution of preventative and surveillance resources. Secondary pulmonary hypertension is a manifestation of advanced COPD, which can be reliably diagnosed by the main Pulmonary Artery (PA) to Ascending Aorta (Ao) ratio. In effect, a PA diameter to Ao diameter ratio of greater than 1 has been demonstrated to be a reliable marker of increased pulmonary arterial pressure. Although clinically valuable and readily visualized, the manual assessment of the PA and the Ao diameters is time consuming and under-reported. The present study describes a non invasive method to measure the diameters of both the Ao and the PA from contrast-enhanced chest Computed Tomography (CT). The solution applies deep learning techniques in order to select the correct axial slice to measure, and to segment both arteries. The system achieves test Pearson correlation coefficient scores of 93% for the Ao and 92% for the PA. To the best of our knowledge, it is the first such fully automated solution.