Classification of Central Venous Catheter Tip Position on Chest X-ray Using Artificial Intelligence

Abstract

Recent studies utilizing deep convolutional neural networks (CNN) have described the central venous catheter (CVC) on chest radiography images. However, there have been no studies for the classification of the CVC tip position with a definite criterion on the chest radiograph. This study aimed to develop an algorithm for the automatic classification of proper depth with the application of automatic segmentation of the trachea and the CVC on chest radiographs using a deep CNN. This was a retrospective study that used plain chest supine anteroposterior radiographs. The trachea and CVC were segmented on images and three labels (shallow, proper, and deep position) were assigned based on the vertical distance between the tracheal carina and CVC tip. We used a two-stage approach model for the automatic segmentation of the trachea and CVC with U-net++ and automatic classification of CVC placement with EfficientNet B4. The primary outcome was a successful three-label classification through five-fold validations with segmented images and a test with segmentation-free images. Of a total of 808 images, 207 images were manually segmented and the overall accuracy of the five-fold validation for the classification of three-class labels (mean (SD)) of five-fold validation was 0.76 (0.03). In the test for classification with 601 segmentation-free images, the average accuracy, precision, recall, and F1-score were 0.82, 0.73, 0.73, and 0.73, respectively. We achieved the highest accuracy value of 0.91 in the shallow position label, while the highest F1-score was 0.82 in the deep position label. A deep CNN can achieve a comparative performance in the classification of the CVC position based on the distance from the carina to the CVC tip as well as automatic segmentation of the trachea and CVC on plain chest radiographs.

Artificial Intelligence to Assess Tracheal Tubes and Central Venous Catheters in Chest Radiographs Using an Algorithmic Approach With Adjustable Positioning Definitions

“…4A), the algorithm outperformed detection rates of traditional computer-aided detection models (eg, 75% according to Keller et al 36 ). Results of the very few recent studies that also evaluate CVC positioning quality (eg, Jung et al 26 ) are not conclusively comparable with our triaging scenario due to different definitions of good CVC tip positioning.…”

“…The AI algorithm comprises multiple CNNs to segment anatomical structures. More specifically, we exploit the well-known U-Net architecture 26,27 for the segmentation of clavicular joints (4-layer depth, 32 feature maps), right atrium (4-layer depth, 16 feature maps), and carina (6-layer depth, 64 feature maps). For lines and tubes, we introduce a novel multitask CNN to mimic the clinical workflow as depicted in Figure 1B: the medical image is first analyzed by "Subnet 1" (U-Net of 6-layer depth and 64 feature maps) to determine the number of CVCs (linear classifier head with at the output of the U-Net encoder) and to segment the respective courses.…”

Machine Vision and Image Analysis in Anesthesia: Narrative Review and Future Prospects

“…Furthermore, the algorithms could be used to automatically populate ETT presence and insertion depth on radiology reports and critical care documentation. Similar uses of MV have been reported for central venous catheter 35 and feeding tube placement 36 on chest radiographs.…”