ArticleJournal of digital imaging2021
Automatic Detection and Classification of Multiple Catheters in Neonatal Radiographs with Deep Learning.
Article in Journal of digital imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review.Pediatric radiology · 2022Pooled it
- A two-stage deep learning pipeline for automated detection and localization of endotracheal tubes on pediatric chest radiographs: a pilot study.Pediatric radiology · 2026Article
- 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
- Artificial intelligence aids doctors in diagnosing necrotizing enterocolitis and predicting surgery using abdominal radiographs: a multicenter study.Quantitative imaging in medicine and surgery · 2025Article
- Improving Outcomes Through Radiologic Verification: A Quality Improvement Approach to Nasogastric Tube Placement.Cureus · 2025Article
- Towards multimodal visualization of esophageal motility: fusion of manometry, impedance, and videofluoroscopic image sequences.International journal of computer assisted radiology and surgery · 2025Article
- Anatomically Guided Deep Learning System for Right Internal Jugular Line (RIJL) Segmentation and Tip Localization in Chest X-Ray.Life (Basel, Switzerland) · 2025Article
- Neurosonographic Classification in Premature Infants Receiving Omega-3 Supplementation Using Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2024Article
- Analysis of Line and Tube Detection Performance of a Chest X-ray Deep Learning Model to Evaluate Hidden Stratification.Diagnostics (Basel, Switzerland) · 2023Article
- Easy-Ensemble Augmented-Shot-Y-Shaped Learning: State-of-the-Art Few-Shot Classification with Simple Components.Journal of imaging · 2022Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
We develop and evaluate a deep learning algorithm to classify multiple catheters on neonatal chest and abdominal radiographs. A convolutional neural network (CNN) was trained using a dataset of 777 neonatal chest and abdominal radiographs, with a split of 81%-9%-10% for training-validation-testing, respectively. We employed ResNet-50 (a CNN), pre-trained on ImageNet. Ground truth labelling was limited to tagging each image to indicate the presence or absence of endotracheal tubes (ETTs), nasogastric tubes (NGTs), and umbilical arterial and venous catheters (UACs, UVCs). The dataset included 561 images containing two or more catheters, 167 images with only one, and 49 with none. Performance was measured with average precision (AP), calculated from the area under the precision-recall curve. On our test data, the algorithm achieved an overall AP (95% confidence interval) of 0.977 (0.679-0.999) for NGTs, 0.989 (0.751-1.000) for ETTs, 0.979 (0.873-0.997) for UACs, and 0.937 (0.785-0.984) for UVCs. Performance was similar for the set of 58 test images consisting of two or more catheters, with an AP of 0.975 (0.255-1.000) for NGTs, 0.997 (0.009-1.000) for ETTs, 0.981 (0.797-0.998) for UACs, and 0.937 (0.689-0.990) for UVCs. Our network thus achieves strong performance in the simultaneous detection of these four catheter types. Radiologists may use such an algorithm as a time-saving mechanism to automate reporting of catheters on radiographs.
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