# Computer-Aided Assessment of Catheters and Tubes on Radiographs: How Good is Artificial Intelligence for Assessment?

arXiv: https://arxiv.org/abs/2002.03413 · HTML: https://mlsanity.com/paper/2002.03413/

## Key facts

- Cited by 5 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 9 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [Challenges for Responsible AI Design and Workflow Integration in Healthcare: A Case Study of Automatic Feeding Tube Qualification in Radiology](https://mlsanity.com/paper/2405.05299/) (2024)
- [Uncertainty-Aware Compositional Localization and Placement Assessment of Catheters and Tubes in Chest X-Rays](https://mlsanity.com/paper/2608.11288/) (2026)
- [Development of Automatic Endotracheal Tube and Carina Detection on Portable Supine Chest Radiographs using Artificial Intelligence](https://mlsanity.com/paper/2206.03017/) (2022)
- [Automated Precision Localization of Peripherally Inserted Central Catheter Tip through Model-Agnostic Multi-Stage Networks](https://mlsanity.com/paper/2206.06730/) (2022)
- [Closing the Performance Gap Between AI and Radiologists in Chest X-Ray Reporting](https://mlsanity.com/paper/2511.21735/) (2025)

## Abstract (arXiv)

Catheters are the second most common abnormal finding on radiographs. The position of catheters must be assessed on all radiographs, as serious complications can arise if catheters are malpositioned. However, due to the large number of radiographs performed each day, there can be substantial delays between the time a radiograph is performed and when it is interpreted by a radiologist. Computer-aided approaches hold the potential to assist in prioritizing radiographs with potentially malpositioned catheters for interpretation and automatically insert text indicating the placement of catheters in radiology reports, thereby improving radiologists' efficiency. After 50 years of research in computer-aided diagnosis, there is still a paucity of study in this area. With the development of deep learning approaches, the problem of catheter assessment is far more solvable. Therefore, we have performed a review of current algorithms and identified key challenges in building a reliable computer-aided diagnosis system for assessment of catheters on radiographs. This review may serve to further the development of machine learning approaches for this important use case.