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.
翻译:内插的中央导管(PICCs)被广泛用作具有代表性的中央静脉管(CVCs)之一。然而,由于其长期的内血管内存接触且低感染性,PICCs在高频的倾斜误判中有一个致命的缺陷,增加了穿孔、栓塞的风险,以及心脏病等并发症。为了自动和准确地检测,利用最新的深度学习技术(DL)做出了各种尝试。然而,即使采用这些方法,仍然很难确定倾斜位置,因为多倍的断裂(MFP)出现在预测提示之前所需的PIC线的预测和提取过程中。这项研究旨在开发一种普遍适用于现有模型的孔径偏差、栓塞和心脏心律等高位的风险。为了实现这一点,我们提议采用一个基于多阶段的 DL 框架, 后处理CPC 模型的提取结果(MFCs), 多重的裂变速(MFC) 现象(MFC) 已经由现有的平均运行率(MFC) 降到了目前的平均速度(MFC) 。