Automated Detection and Type Classification of Central Venous Catheters in Chest X-Rays
This provides a practical solution for critical care settings by improving the accuracy and efficiency of catheter identification in radiology reports.
The paper tackled the problem of automatically detecting and classifying central venous catheters in chest X-rays, achieving 85.2% accuracy in detection and 95.2% precision in type classification on a dataset of over 10,000 images.
Central venous catheters (CVCs) are commonly used in critical care settings for monitoring body functions and administering medications. They are often described in radiology reports by referring to their presence, identity and placement. In this paper, we address the problem of automatic detection of their presence and identity through automated segmentation using deep learning networks and classification based on their intersection with previously learned shape priors from clinician annotations of CVCs. The results not only outperform existing methods of catheter detection achieving 85.2% accuracy at 91.6% precision, but also enable high precision (95.2%) classification of catheter types on a large dataset of over 10,000 chest X-rays, presenting a robust and practical solution to this problem.