Quantification of pulmonary edema using automated lung segmentation on computed tomography in mechanically ventilated patients with acute respiratory distress syndrome.

IF 2.8 Q2 CRITICAL CARE MEDICINE
Alice Marguerite Conrad, Julia Zimmermann, David Mohr, Matthias F Froelich, Alexander Hertel, Nils Rathmann, Christoph Boesing, Manfred Thiel, Stefan O Schoenberg, Joerg Krebs, Thomas Luecke, Patricia R M Rocco, Matthias Otto
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引用次数: 0

Abstract

Background: Quantification of pulmonary edema in patients with acute respiratory distress syndrome (ARDS) by chest computed tomography (CT) scan has not been validated in routine diagnostics due to its complexity and time-consuming nature. Therefore, the single-indicator transpulmonary thermodilution (TPTD) technique to measure extravascular lung water (EVLW) has been used in the clinical setting. Advances in artificial intelligence (AI) have now enabled CT images of inhomogeneous lungs to be segmented automatically by an intensive care physician with no prior radiology training within a relatively short time. Nevertheless, there is a paucity of data validating the quantification of pulmonary edema using automated lung segmentation on CT compared with TPTD.

Methods: A retrospective study (January 2016 to December 2021) analyzed patients with ARDS, admitted to the intensive care unit of the Department of Anesthesiology and Critical Care Medicine, University Hospital Mannheim, who underwent a chest CT scan and hemodynamic monitoring using TPTD at the same time. Pulmonary edema was estimated using manually and automated lung segmentation on CT and then compared to the pulmonary edema calculated from EVLW determined using TPTD.

Results: 145 comparative measurements of pulmonary edema with TPTD and CT were included in the study. Estimating pulmonary edema using either automated lung segmentation on CT or TPTD showed a low bias overall (- 104 ml) but wide levels of agreement (upper: 936 ml, lower: - 1144 ml). In 13% of the analyzed CT scans, the agreement between the segmentation of the AI algorithm and a dedicated investigator was poor. Manual segmentation and automated segmentation adjusted for contrast agent did not improve the agreement levels.

Conclusions: Automated lung segmentation on CT can be considered an unbiased but imprecise measurement of pulmonary edema in mechanically ventilated patients with ARDS.

在急性呼吸窘迫综合征的机械通气患者中,利用计算机断层扫描的自动肺分割技术量化肺水肿。
背景:通过胸部计算机断层扫描(CT)对急性呼吸窘迫综合征(ARDS)患者的肺水肿进行定量,由于其复杂性和耗时性,尚未在常规诊断中得到验证。因此,临床上一直使用单指标经肺热稀释(TPTD)技术来测量血管外肺水(EVLW)。目前,人工智能(AI)技术的进步已使重症监护医生无需接受过放射学培训,就能在较短时间内自动分割不均匀肺部的 CT 图像。尽管如此,与 TPTD 相比,使用 CT 肺部自动分割对肺水肿进行量化的验证数据仍然很少:一项回顾性研究(2016 年 1 月至 2021 年 12 月)分析了曼海姆大学医院麻醉学和重症医学科重症监护室收治的 ARDS 患者,这些患者同时接受了胸部 CT 扫描和使用 TPTD 进行的血液动力学监测。通过 CT 上的手动和自动肺分割估算肺水肿,然后与使用 TPTD 确定的 EVLW 计算出的肺水肿进行比较:研究共纳入了 145 项使用 TPTD 和 CT 进行的肺水肿对比测量结果。使用 CT 自动肺分割或 TPTD 估算肺水肿的总体偏差较小(- 104 毫升),但一致性较高(上限:936 毫升,下限:- 1144 毫升)。在 13% 的 CT 扫描分析中,人工智能算法和专职研究人员的分割结果一致性较差。手动分割和根据造影剂调整的自动分割并没有提高一致性水平:CT上的自动肺分割可被视为对ARDS机械通气患者肺水肿的一种无偏见但不精确的测量方法。
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来源期刊
Intensive Care Medicine Experimental
Intensive Care Medicine Experimental CRITICAL CARE MEDICINE-
CiteScore
5.10
自引率
2.90%
发文量
48
审稿时长
13 weeks
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