ReviewRadiology. Artificial intelligence2020
Computer-aided Assessment of Catheters and Tubes on Radiographs: How Good Is Artificial Intelligence for Assessment?
Review in Radiology. Artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review.Pediatric radiology · 2022Pooled it
- Review of Artificial Intelligence for Automated Assessment of Lines, Drains, and Airways on Pediatric and Adult Chest Radiographs.Pediatric reports · 2026Review
- The role of artificial intelligence in paediatric abdominal imaging.Pediatric radiology · 2026Review
- Automatic segmentation and labeling of T1, T7, and T12 thoracic vertebrae in neonatal chest radiographs: a deep learning approach using nnU-Net framework.Frontiers in pediatrics · 2026Article
- Towards multimodal visualization of esophageal motility: fusion of manometry, impedance, and videofluoroscopic image sequences.International journal of computer assisted radiology and surgery · 2025Article
- Deep Learning-Based Localization and Detection of Malpositioned Nasogastric Tubes on Portable Supine Chest X-Rays in Intensive Care and Emergency Medicine: A Multi-center Retrospective Study.Journal of imaging informatics in medicine · 2025Article
- Deep learning for tubes and lines detection in critical illness: Generalizability and comparison with residents.European journal of radiology open · 2024Article
- Open Access Data and Deep Learning for Cardiac Device Identification on Standard DICOM and Smartphone-based Chest Radiographs.Radiology. Artificial intelligence · 2024Article
- Artificial intelligence in the detection of non-biological materials.Emergency radiology · 2024Review
- Detection and position evaluation of chest percutaneous drainage catheter on chest radiographs using deep learning.PloS one · 2024Article
- Analysis of Line and Tube Detection Performance of a Chest X-ray Deep Learning Model to Evaluate Hidden Stratification.Diagnostics (Basel, Switzerland) · 2023Article
- Chest X-ray Interpretation: Detecting Devices and Device-Related Complications.Diagnostics (Basel, Switzerland) · 2023Review
- Classification of Central Venous Catheter Tip Position on Chest X-ray Using Artificial Intelligence.Journal of personalized medicine · 2022Article
- Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective.Pediatric radiology · 2022Review
- Position Classification of the Endotracheal Tube with Automatic Segmentation of the Trachea and the Tube on Plain Chest Radiography Using Deep Convolutional Neural Network.Journal of personalized medicine · 2022Article
- Tubular shape aware data generation for segmentation in medical imaging.International journal of computer assisted radiology and surgery · 2022Article
- Automatic Detection and Classification of Multiple Catheters in Neonatal Radiographs with Deep Learning.Journal of digital imaging · 2021Article
- Artificial intelligence in paediatric radiology: Future opportunities.The British journal of radiology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Catheters are the second most common abnormal finding on radiographs. The position of catheters must be assessed on all radiographs because serious complications can arise if catheters are malpositioned. However, due to the large number of radiographs obtained each day, there can be substantial delays between the time a radiograph is obtained 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. This review provides an overview of current algorithms and identifies 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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.