Peer-Reviewed Publication
Diagnostics (Basel)2023;13(14)July 9, 2023Journal Article
Analysis of Line and Tube Detection Performance of a Chest X-ray Deep Learning Model to Evaluate Hidden Stratification.
Cyril H M Tang1,2, Jarrel C Y Seah1,3, Hassan K Ahmad1, Michael R Milne1, Jeffrey B Wardman1, Quinlan D Buchlak1,4,5, Nazanin Esmaili4,6, John F Lambert1, Catherine M Jones1,7,8,9
1Annalise.ai, Sydney, NSW 2000, Australia.
2Intensive Care Unit, Gosford Hospital, Sydney, NSW 2250, Australia.
3Department of Radiology, Alfred Health, Melbourne, VIC 3004, Australia.
4School of Medicine, The University of Notre Dame Australia, Sydney, NSW 2007, Australia.
5Department of Neurosurgery, Monash Health, Melbourne, VIC 3168, Australia.
6Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia.
7I-MED Radiology Network, Brisbane, QLD 4006, Australia.
8School of Public and Preventive Health, Monash University, Clayton, VIC 3800, Australia.
9Department of Clinical Imaging Science, University of Sydney, Sydney, NSW 2006, Australia.
Abstract
This retrospective case-control study evaluated the diagnostic performance of a commercially available chest radiography deep convolutional neural network (DCNN) in identifying the presence and position of central venous catheters, enteric tubes, and endotracheal tubes, in addition to a subgroup analysis of different types of lines/tubes. A held-out test dataset of 2568 studies was sourced from co…
Create a free account to keep reading
Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.
Want unlimited research access? See Pro plans