ArticleJournal of digital imaging2018
A Deep-Learning System for Fully-Automated Peripherally Inserted Central Catheter (PICC) Tip Detection.
Article in Journal of digital imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- The implementation of artificial intelligence in upper extremity surgery: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Imaging-Based Artificial Intelligence in Vascular and Interventional Radiology: A Narrative Review.Journal of imaging informatics in medicine · 2026Review
- Review of Artificial Intelligence for Automated Assessment of Lines, Drains, and Airways on Pediatric and Adult Chest Radiographs.Pediatric reports · 2026Review
- Topology preserving embedded network for PICC segmentation in pediatric X ray images.NPJ digital medicine · 2026Article
- 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
- Risk Management and Patient Safety in the Artificial Intelligence Era: A Systematic Review.Healthcare (Basel, Switzerland) · 2024Review
- 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
- Image augmentation and automated measurement of endotracheal-tube-to-carina distance on chest radiographs in intensive care unit using a deep learning model with external validation.Critical care (London, England) · 2023Article
- Classification of Central Venous Catheter Tip Position on Chest X-ray Using Artificial Intelligence.Journal of personalized medicine · 2022Article
- Tubular shape aware data generation for segmentation in medical imaging.International journal of computer assisted radiology and surgery · 2022Article
- Deep-learning-assisted algorithm for catheter reconstruction during MR-only gynecological interstitial brachytherapy.Journal of applied clinical medical physics · 2022Article
- Machine Learning for Detection of Correct Peripherally Inserted Central Catheter Tip Position from Radiology Reports in Infants.Applied clinical informatics · 2021Article
- Automatic Detection and Classification of Multiple Catheters in Neonatal Radiographs with Deep Learning.Journal of digital imaging · 2021Article
- The influential factors and intervention strategies that engage malignant cancer patients in health-promoting behaviors during PICC line maintenance.American journal of translational research · 2021Article
- Automatic multi-catheter detection using deeply supervised convolutional neural network in MRI-guided HDR prostate brachytherapy.Medical physics · 2020Article
- Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges.Korean journal of radiology · 2020Review
- Automatic Catheter and Tube Detection in Pediatric X-ray Images Using a Scale-Recurrent Network and Synthetic Data.Journal of digital imaging · 2020Article
- Computer-aided Assessment of Catheters and Tubes on Radiographs: How Good Is Artificial Intelligence for Assessment?Radiology. Artificial intelligence · 2020Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A peripherally inserted central catheter (PICC) is a thin catheter that is inserted via arm veins and threaded near the heart, providing intravenous access. The final catheter tip position is always confirmed on a chest radiograph (CXR) immediately after insertion since malpositioned PICCs can cause potentially life-threatening complications. Although radiologists interpret PICC tip location with high accuracy, delays in interpretation can be significant. In this study, we proposed a fully-automated, deep-learning system with a cascading segmentation AI system containing two fully convolutional neural networks for detecting a PICC line and its tip location. A preprocessing module performed image quality and dimension normalization, and a post-processing module found the PICC tip accurately by pruning false positives. Our best model, trained on 400 training cases and selectively tuned on 50 validation cases, obtained absolute distances from ground truth with a mean of 3.10 mm, a standard deviation of 2.03 mm, and a root mean squares error (RMSE) of 3.71 mm on 150 held-out test cases. This system could help speed confirmation of PICC position and further be generalized to include other types of vascular access and therapeutic support devices.
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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.