论文标题
通过模型不合时式的多阶段网络自动插入的中央导管尖端的自动精度定位
Automated Precision Localization of Peripherally Inserted Central Catheter Tip through Model-Agnostic Multi-Stage Networks
论文作者
论文摘要
外围插入的中央导管（PICC）由于其长期的血管内通道且感染率低，已被广泛用作代表性的中央静脉线（CVC）之一。但是，PICC的尖端错位频率很高，增加了刺穿，栓塞和心律不齐等并发症的风险。为了自动和精确地检测到它，使用了最新的深度学习（DL）技术进行了各种尝试。但是，即使采用了这些方法，实际上仍然很难确定尖端位置，因为多个片段现象（MFP）发生在预测和提取PICC线之前预测尖端之前所需的PICC线。这项研究旨在开发一种通常应用于现有模型的系统，并通过删除模型输出的MF来更准确地恢复PICC线，从而精确地定位了检测其处置的实际尖端位置。为了实现这一目标，我们提出了一个基于多阶段DL的框架，后处理了现有技术的PICC线提取结果。根据是否将MFCN应用于五个常规模型，将每个根平方误差（RMSE）和MFP发病率比较性能。在内部验证中，当将MFCN应用于现有单个模型时，MFP平均提高了45％。 RMSE从平均26.85mm（17.16到35.80mm）到9.72mm（9.37至10.98mm）的平均增加了63％以上。在外部验证中，当应用MFCN时，MFP的发病率平均下降32％，RMSE平均降低65 \％。因此，通过应用提出的MFCN，我们观察到与现有模型相比，PICC尖端位置的显着/一致检测性能提高了。
Peripherally inserted central catheters (PICCs) have been widely used as one of the representative central venous lines (CVCs) due to their long-term intravascular access with low infectivity. However, PICCs have a fatal drawback of a high frequency of tip mispositions, increasing the risk of puncture, embolism, and complications such as cardiac arrhythmias. To automatically and precisely detect it, various attempts have been made by using the latest deep learning (DL) technologies. However, even with these approaches, it is still practically difficult to determine the tip location because the multiple fragments phenomenon (MFP) occurs in the process of predicting and extracting the PICC line required before predicting the tip. This study aimed to develop a system generally applied to existing models and to restore the PICC line more exactly by removing the MFs of the model output, thereby precisely localizing the actual tip position for detecting its disposition. To achieve this, we proposed a multi-stage DL-based framework post-processing the PICC line extraction result of the existing technology. The performance was compared by each root mean squared error (RMSE) and MFP incidence rate according to whether or not MFCN is applied to five conventional models. In internal validation, when MFCN was applied to the existing single model, MFP was improved by an average of 45%. The RMSE was improved by over 63% from an average of 26.85mm (17.16 to 35.80mm) to 9.72mm (9.37 to 10.98mm). In external validation, when MFCN was applied, the MFP incidence rate decreased by an average of 32% and the RMSE decreased by an average of 65\%. Therefore, by applying the proposed MFCN, we observed the significant/consistent detection performance improvement of PICC tip location compared to the existing model.
