Landmark-based graph convolutional neural network for interpretable catheter malposition detection
Year: 2025
Type: article
Abstract: Hospital patients can have catheters and lines inserted during their admission to give medicines or to do medical tests quickly, especially central venous catheters (CVCs). Following the insertion process, the position of the catheter is commonly verified based on the X-ray images to detect the malposition, avoiding a series of complications. Recently, deep learning frameworks have shown their potential to assist in detecting the malposition of the CVC in X-ray images. However, these deep learning approaches meet three main challenges: 1) The absence and ambiguous anatomical landmarks in X-ray images make the implementation of the landmark-annotation-based framework particularly challenging. 2) Most of the existing deep learning frameworks have a large number of parameters and a large computation workload. 3) The feature significance for the deep-learning framework to detect the misplaced catheter is not well studied, which may impact the understanding of the deep-learning model results. Therefore, a framework based on a graph convolutional neural network (GCN) is proposed in this work to detect catheter malposition. It achieves an AUC of 0.867 with 0.0229 million parameters. Even with a landmark missing, it still achieves an AUC of 0.855. It is demonstrated that the proposed framework can tolerate the missing and ambiguous landmarks in chest X-rays (CXRs) and get acceptable detection performance. Additionally, the proposed method achieves state-of-the-art performance with a relatively small number of parameters. Moreover, the features much more important for catheter malposition detection can be verified by the proposed framework to make the catheter malposition detection framework more understandable.
Source: Knowledge-Based Systems
Authors: Yuhan Wang, Hak Keung Lam
Institution: King's College London
Language: English
Topic: COVID-19 diagnosis using AI
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,430
OpenAlex ID: W4416818683