在计算机断层扫描 (CT) 中准确采集和分配不同的对比增强阶段对于临床医生和人工智能编排以选择最合适的系列进行分析至关重要。然而，这些信息通常是从 CT 元数据中提取的，这通常是错误的。本研究旨在开发一种自动管道，用于对静脉 (IV) 造影剂阶段进行分类，并另外识别胃肠道 (GIT) 中的造影剂。这项回顾性研究使用了 2016 年 1 月 4 日至 9 月 12 日期间在调查机构收集的 1200 张 CT 扫描图像、2022 年和来自癌症影像档案库的多个中心的 240 个 CT 扫描进行外部验证。使用开源分割算法TotalSegmentator来识别感兴趣区域（肺动脉、主动脉、胃、门/脾静脉、肝脏、门静脉/肝静脉、下腔静脉、十二指肠、小肠、结肠、左/右肾） 、膀胱）和机器学习分类器通过 5 倍交叉验证进行训练，以对 IV 造影期（非造影剂、肺动脉、动脉、静脉和尿路造影）和 GIT 造影增强进行分类。使用受试者工作特征曲线下面积 (AUC) 和 95% 置信区间 (CI) 评估集成的性能。对于 IV 期分类任务，内部测试集获得以下 AUC 分数：99.59% [非造影期 95% CI，99.58-99.63]，肺动脉期 99.50% [95% CI，99.49-99.52]，动脉期 99.13% [95% CI，99.10-99.15]，99.8% [静脉期为 95% CI，99.79-99.81]，尿路造影期为 99.7% [95% CI，99.68-99.7]。对于外部数据集，第一和第二注释器的所有对比相的平均 AUC 分别为 97.33% [95% CI, 97.27-97.35] 和 97.38% [95% CI, 97.34-97.41]。 GIT 中的造影剂在内部数据集中的 AUC 为 99.90% [95% CI, 99.89-99.9]，而在外部数据集中，AUC 为 99.73% [95% CI, 99.71-99.73] 和 99.31 % [95% CI, 99.27-99.33] 分别通过第一个和第二个注释器实现。开源分割网络和分类器的集成有效地对对比相位进行分类，并使用解剖标志识别 GIT 对比增强。版权所有 © 2024 Wolters Kluwer Health ，公司保留所有权利。

Accurately acquiring and assigning different contrast-enhanced phases in computed tomography (CT) is relevant for clinicians and for artificial intelligence orchestration to select the most appropriate series for analysis. However, this information is commonly extracted from the CT metadata, which is often wrong. This study aimed at developing an automatic pipeline for classifying intravenous (IV) contrast phases and additionally for identifying contrast media in the gastrointestinal tract (GIT).This retrospective study used 1200 CT scans collected at the investigating institution between January 4, 2016 and September 12, 2022, and 240 CT scans from multiple centers from The Cancer Imaging Archive for external validation. The open-source segmentation algorithm TotalSegmentator was used to identify regions of interest (pulmonary artery, aorta, stomach, portal/splenic vein, liver, portal vein/hepatic veins, inferior vena cava, duodenum, small bowel, colon, left/right kidney, urinary bladder), and machine learning classifiers were trained with 5-fold cross-validation to classify IV contrast phases (noncontrast, pulmonary arterial, arterial, venous, and urographic) and GIT contrast enhancement. The performance of the ensembles was evaluated using the receiver operating characteristic area under the curve (AUC) and 95% confidence intervals (CIs).For the IV phase classification task, the following AUC scores were obtained for the internal test set: 99.59% [95% CI, 99.58-99.63] for the noncontrast phase, 99.50% [95% CI, 99.49-99.52] for the pulmonary-arterial phase, 99.13% [95% CI, 99.10-99.15] for the arterial phase, 99.8% [95% CI, 99.79-99.81] for the venous phase, and 99.7% [95% CI, 99.68-99.7] for the urographic phase. For the external dataset, a mean AUC of 97.33% [95% CI, 97.27-97.35] and 97.38% [95% CI, 97.34-97.41] was achieved for all contrast phases for the first and second annotators, respectively. Contrast media in the GIT could be identified with an AUC of 99.90% [95% CI, 99.89-99.9] in the internal dataset, whereas in the external dataset, an AUC of 99.73% [95% CI, 99.71-99.73] and 99.31% [95% CI, 99.27-99.33] was achieved with the first and second annotator, respectively.The integration of open-source segmentation networks and classifiers effectively classified contrast phases and identified GIT contrast enhancement using anatomical landmarks.Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.