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

• Main Authors: Chettrit, David, Amitai, Orna Bregman, Tamir, Itamar, Bar, Amir, Elnekave, Eldad
• Format: Journal Article
• Language: English
• Published: 28-05-2019
• Subjects: Computer Science - Computer Vision and Pattern Recognition
• DOI: 10.48550/arxiv.1905.11773

Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis,
109500O 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.