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

2019-05-28 12:39:05
David Chettrit, Orna Bregman Amitai, Itamar Tamir, Amir Bar, Eldad Elnekave

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.

Abstract (translated)

慢性阻塞性肺疾病(COPD)是全球发病率和死亡率的主要原因。确定最有恶化风险的人将有助于更有效地分配预防和监测资源。继发性肺动脉高压是晚期慢性阻塞性肺病的一种表现,通过主肺动脉(PA)与升主动脉(AO)的比值可以可靠地诊断。事实上,PA直径与AO直径之比大于1已被证明是肺动脉压升高的可靠标志。虽然临床价值高且易于观察,但人工评估PA和AO直径耗时且报告不足。本研究描述了一种非侵入性的方法来测量造影增强胸部计算机断层扫描(CT)的AO和PA的直径。该解决方案应用深度学习技术来选择要测量的正确轴向切片,并分割两条动脉。系统的AO测试皮尔逊相关系数得分为93%,PA测试皮尔逊相关系数得分为92%,据我们所知,这是第一个完全自动化的解决方案。

URL

https://arxiv.org/abs/1905.11773

PDF

https://arxiv.org/pdf/1905.11773.pdf


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