Artificial Intelligence-based Fully Automated Per Lobe Segmentation and Emphysema-quantification Based on Chest Computed Tomography Compared With Global Initiative for Chronic Obstructive Lung Disease Severity of Smokers
Artificial Intelligence-based Fully Automated Per Lobe Segmentation and Emphysema-quantification Based on Chest Computed Tomography Compared With Global Initiative for Chronic Obstructive Lung Disease Severity of Smokers
复制标题
基于人工智能的全自动肺叶分割和基于胸部计算机断层扫描的肺气肿量化与吸烟者慢性阻塞性肺疾病严重程度全球倡议的比较
DOI：
10.1097/rti.0000000000000500
复制
发表时间：
2020-05-01
影响因子：
3.3
通讯作者：
Schoepf, U. Joseph
中科院分区：
文献类型：
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作者：
Fischer, Andreas M.;Varga-Szemes, Akos;Schoepf, U. Joseph
目的：本研究的目的是评价一种基于人工智能（AI）的原型算法，用于胸部计算机断层扫描的全自动每叶分割和肺气肿量化（EQ），并将其与慢性阻塞性肺疾病（COPD）患者的全球倡议（GOLD）严重程度分类进行比较。研究方法：在6个月内接受胸部计算机断层扫描采集和肺功能测定的患者（n=137）回顾性纳入本机构审查委员会批准的符合健康保险携带和责任法案的研究。使用患者特异性肺量测定数据（包括1秒用力呼气量、用力肺活量和1秒用力呼气量/用力肺活量比值（Tiffneau-Index））将患者分配至各自的GOLD I至IV期。使用AI-RAD Companion软件原型（Siemens Healthineers）进行肺叶分割，深度卷积图像到图像网络，并在每个肺叶中量化肺气肿以检测低衰减体积。结果：全肺EQ与GOLD分期之间有较强的相关性（rho= 0.88，P
Objectives: The objective of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm for the fully automated per lobe segmentation and emphysema quantification (EQ) on chest-computed tomography as it compares to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) severity classification of chronic obstructive pulmonary disease (COPD) patients. Methods: Patients (n=137) who underwent chest-computed tomography acquisition and spirometry within 6 months were retrospectively included in this Institutional Review Board-approved and Health Insurance Portability and Accountability Act-compliant study. Patient-specific spirometry data, which included forced expiratory volume in 1 second, forced vital capacity, and the forced expiratory volume in 1 second/forced vital capacity ratio (Tiffeneau-Index), were used to assign patients to their respective GOLD stage I to IV. Lung lobe segmentation was carried out using AI-RAD Companion software prototype (Siemens Healthineers), a deep convolution image-to-image network and emphysema was quantified in each lung lobe to detect the low attenuation volume. Results: A strong correlation between the whole-lung-EQ and the GOLD stages was found (rho=0.88,P