Brown Matthew S, Wong Koon-Pong, Shrestha Liza, Wahi-Anwar Muhammad, Daly Morgan, Foster George, Abtin Fereidoun, Ruchalski Kathleen L, Goldin Jonathan G, Enzmann Dieter
Department of Radiological Sciences, Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine at UCLA, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024.
Department of Radiological Sciences, Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine at UCLA, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024.
Acad Radiol. 2023 Mar;30(3):412-420. doi: 10.1016/j.acra.2022.04.022. Epub 2022 May 27.
To develop artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest X-rays (CXRs) and evaluate whether it can move into clinical validation as a quality improvement tool.
A retrospective data set including 2000 de-identified images from intensive care unit patients was split into 1488 for training and 512 for testing. AI was developed to automatically identify the ETT, trachea, and carina using semantically embedded neural networks that combine a declarative knowledge base with deep neural networks. To check the ETT tip placement, a "safe zone" was computed as the region inside the trachea and 3-7 cm above the carina. Two AI outputs were evaluated: (1) ETT overlay, (2) ETT misplacement alert messages. Clinically relevant performance metrics were compared against prespecified thresholds of >85% overlay accuracy and positive predictive value (PPV) > 30% and negative predictive value NPV > 95% for alerts to move into clinical validation.
An ETT was present in 285 of 512 test cases. The AI detected 95% (271/285) of ETTs, 233 (86%) of these with accurate tip localization. The system (correctly) did not generate an ETT overlay in 221/227 CXRs where the tube was absent for an overall overlay accuracy of 89% (454/512). The alert messages indicating that either the ETT was misplaced or not detected had a PPV of 83% (265/320) and NPV of 98% (188/192).
The chest X-ray AI met prespecified performance thresholds to move into clinical validation.
开发一种人工智能（AI）系统，以辅助检查胸部X光片（CXR）上气管内插管（ETT）的位置，并评估其是否可作为质量改进工具进入临床验证阶段。
一个回顾性数据集，包含来自重症监护病房患者的2000张去识别化图像，被分为1488张用于训练，512张用于测试。利用将声明性知识库与深度神经网络相结合的语义嵌入神经网络开发人工智能，以自动识别ETT、气管和隆突。为检查ETT尖端位置，计算出一个“安全区”，即气管内且在隆突上方3 - 7厘米的区域。评估了两种人工智能输出：（1）ETT覆盖图，（2）ETT误置警报信息。将临床相关性能指标与预先设定的阈值进行比较，即覆盖准确率>85%、阳性预测值（PPV）>30%以及阴性预测值（NPV）>95%，以确定是否进入临床验证阶段。
在512个测试病例中，有285个存在ETT。人工智能检测到了95%（271/285）的ETT，其中233个（86%）的尖端定位准确。在227张没有插管的CXR中，系统（正确地）没有生成ETT覆盖图，总体覆盖准确率为89%（454/512）。表明ETT误置或未被检测到的警报信息的PPV为83%（265/320），NPV为98%（188/192）。
胸部X光人工智能达到了进入临床验证的预先设定性能阈值。
