# Automated Detection and Type Classification of Central Venous Catheters in Chest X-Rays

## TL;DR

This paper presents a deep learning-based method for automatic detection and classification of central venous catheters in chest X-rays, achieving high accuracy and precision, thus improving clinical workflow and diagnosis.

## Contribution

It introduces a novel deep learning approach combining segmentation and shape priors for CVC detection and classification, outperforming existing methods.

## Key findings

- Achieved 85.2% detection accuracy with 91.6% precision.
- Enabled 95.2% high-precision classification of catheter types.
- Validated on over 10,000 chest X-ray images.

## Abstract

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.