Tang Cyril H M, Seah Jarrel C Y, Ahmad Hassan K, Milne Michael R, Wardman Jeffrey B, Buchlak Quinlan D, Esmaili Nazanin, Lambert John F, Jones Catherine M
Annalise.ai, Sydney, NSW 2000, Australia.
Intensive Care Unit, Gosford Hospital, Sydney, NSW 2250, Australia.
Diagnostics (Basel). 2023 Jul 9;13(14):2317. doi: 10.3390/diagnostics13142317.
This retrospective case-control study evaluated the diagnostic performance of a commercially available chest radiography deep convolutional neural network (DCNN) in identifying the presence and position of central venous catheters, enteric tubes, and endotracheal tubes, in addition to a subgroup analysis of different types of lines/tubes. A held-out test dataset of 2568 studies was sourced from community radiology clinics and hospitals in Australia and the USA, and was then ground-truth labelled for the presence, position, and type of line or tube from the consensus of a thoracic specialist radiologist and an intensive care clinician. DCNN model performance for identifying and assessing the positioning of central venous catheters, enteric tubes, and endotracheal tubes over the entire dataset, as well as within each subgroup, was evaluated. The area under the receiver operating characteristic curve (AUC) was assessed. The DCNN algorithm displayed high performance in detecting the presence of lines and tubes in the test dataset with AUCs > 0.99, and good position classification performance over a subpopulation of ground truth positive cases with AUCs of 0.86-0.91. The subgroup analysis showed that model performance was robust across the various subtypes of lines or tubes, although position classification performance of peripherally inserted central catheters was relatively lower. Our findings indicated that the DCNN algorithm performed well in the detection and position classification of lines and tubes, supporting its use as an assistant for clinicians. Further work is required to evaluate performance in rarer scenarios, as well as in less common subgroups.
这项回顾性病例对照研究评估了一种商用胸部X线摄影深度卷积神经网络（DCNN）在识别中心静脉导管、肠内管和气管内导管的存在及位置方面的诊断性能，此外还对不同类型的导管进行了亚组分析。一个包含2568项研究的保留测试数据集来自澳大利亚和美国的社区放射科诊所及医院，随后由一位胸科专科放射科医生和一位重症监护临床医生达成共识，对导管或管道的存在、位置及类型进行了真值标注。评估了DCNN模型在整个数据集以及每个亚组中识别和评估中心静脉导管、肠内管和气管内导管位置的性能。评估了受试者操作特征曲线（AUC）下的面积。DCNN算法在检测测试数据集中导管和管道的存在方面表现出高性能，AUC>0.99，并且在真值为阳性的亚组中具有良好的位置分类性能，AUC为0.86 - 0.91。亚组分析表明，尽管外周置入中心静脉导管的位置分类性能相对较低，但模型性能在各种类型的导管中都很稳健。我们的研究结果表明，DCNN算法在导管和管道的检测及位置分类方面表现良好，支持其作为临床医生的辅助工具使用。需要进一步开展工作来评估在更罕见场景以及不太常见亚组中的性能。
