Paper Info
Title
A Deep-Learning System for Fully-Automated Peripherally Inserted Central Catheter (PICC) Tip Detection.
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.
Year
DOI
Venue
2018
10.1007/s10278-017-0025-z
J. Digital Imaging
Keywords
Field
DocType
Chest radiograph,Computer-aided detection,Deep learning,Machine learning,PICC,Radiology workflow
Catheter,Chest radiograph,Segmentation,Computer science,Image quality,Vascular access,Artificial intelligence,Radiology,Deep learning,Peripherally inserted central catheter,Standard deviation
Journal
Volume
Issue
ISSN
31
4
1618-727X
Citations 
PageRank 
References 
2
0.39
10
Authors
5
Name
Order
Citations
PageRank
Hyunkwang Lee1273.78
Mohammad Mansouri220.39
Shahein Tajmir3221.97
Michael H. Lev461.17
Synho Do59412.86