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Analysis of Line and Tube Detection Performance of a Chest X-ray Deep Learning Model to Evaluate Hidden Stratification

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 ana...

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Autores principales: Tang, Cyril H. M., Seah, Jarrel C. Y., Ahmad, Hassan K., Milne, Michael R., Wardman, Jeffrey B., Buchlak, Quinlan D., Esmaili, Nazanin, Lambert, John F., Jones, Catherine M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378683/
https://www.ncbi.nlm.nih.gov/pubmed/37510062
http://dx.doi.org/10.3390/diagnostics13142317
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author Tang, Cyril H. M.
Seah, Jarrel C. Y.
Ahmad, Hassan K.
Milne, Michael R.
Wardman, Jeffrey B.
Buchlak, Quinlan D.
Esmaili, Nazanin
Lambert, John F.
Jones, Catherine M.
author_facet Tang, Cyril H. M.
Seah, Jarrel C. Y.
Ahmad, Hassan K.
Milne, Michael R.
Wardman, Jeffrey B.
Buchlak, Quinlan D.
Esmaili, Nazanin
Lambert, John F.
Jones, Catherine M.
author_sort Tang, Cyril H. M.
collection PubMed
description 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 community radiology clinics and hospitals in Australia and the USA, and was then ground-truth labelled for the presence, position, and type of line or tube from the consensus of a thoracic specialist radiologist and an intensive care clinician. DCNN model performance for identifying and assessing the positioning of central venous catheters, enteric tubes, and endotracheal tubes over the entire dataset, as well as within each subgroup, was evaluated. The area under the receiver operating characteristic curve (AUC) was assessed. The DCNN algorithm displayed high performance in detecting the presence of lines and tubes in the test dataset with AUCs > 0.99, and good position classification performance over a subpopulation of ground truth positive cases with AUCs of 0.86–0.91. The subgroup analysis showed that model performance was robust across the various subtypes of lines or tubes, although position classification performance of peripherally inserted central catheters was relatively lower. Our findings indicated that the DCNN algorithm performed well in the detection and position classification of lines and tubes, supporting its use as an assistant for clinicians. Further work is required to evaluate performance in rarer scenarios, as well as in less common subgroups.
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spelling pubmed-103786832023-07-29 Analysis of Line and Tube Detection Performance of a Chest X-ray Deep Learning Model to Evaluate Hidden Stratification Tang, Cyril H. M. Seah, Jarrel C. Y. Ahmad, Hassan K. Milne, Michael R. Wardman, Jeffrey B. Buchlak, Quinlan D. Esmaili, Nazanin Lambert, John F. Jones, Catherine M. Diagnostics (Basel) Article 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 community radiology clinics and hospitals in Australia and the USA, and was then ground-truth labelled for the presence, position, and type of line or tube from the consensus of a thoracic specialist radiologist and an intensive care clinician. DCNN model performance for identifying and assessing the positioning of central venous catheters, enteric tubes, and endotracheal tubes over the entire dataset, as well as within each subgroup, was evaluated. The area under the receiver operating characteristic curve (AUC) was assessed. The DCNN algorithm displayed high performance in detecting the presence of lines and tubes in the test dataset with AUCs > 0.99, and good position classification performance over a subpopulation of ground truth positive cases with AUCs of 0.86–0.91. The subgroup analysis showed that model performance was robust across the various subtypes of lines or tubes, although position classification performance of peripherally inserted central catheters was relatively lower. Our findings indicated that the DCNN algorithm performed well in the detection and position classification of lines and tubes, supporting its use as an assistant for clinicians. Further work is required to evaluate performance in rarer scenarios, as well as in less common subgroups. MDPI 2023-07-09 /pmc/articles/PMC10378683/ /pubmed/37510062 http://dx.doi.org/10.3390/diagnostics13142317 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tang, Cyril H. M.
Seah, Jarrel C. Y.
Ahmad, Hassan K.
Milne, Michael R.
Wardman, Jeffrey B.
Buchlak, Quinlan D.
Esmaili, Nazanin
Lambert, John F.
Jones, Catherine M.
Analysis of Line and Tube Detection Performance of a Chest X-ray Deep Learning Model to Evaluate Hidden Stratification
title Analysis of Line and Tube Detection Performance of a Chest X-ray Deep Learning Model to Evaluate Hidden Stratification
title_full Analysis of Line and Tube Detection Performance of a Chest X-ray Deep Learning Model to Evaluate Hidden Stratification
title_fullStr Analysis of Line and Tube Detection Performance of a Chest X-ray Deep Learning Model to Evaluate Hidden Stratification
title_full_unstemmed Analysis of Line and Tube Detection Performance of a Chest X-ray Deep Learning Model to Evaluate Hidden Stratification
title_short Analysis of Line and Tube Detection Performance of a Chest X-ray Deep Learning Model to Evaluate Hidden Stratification
title_sort analysis of line and tube detection performance of a chest x-ray deep learning model to evaluate hidden stratification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378683/
https://www.ncbi.nlm.nih.gov/pubmed/37510062
http://dx.doi.org/10.3390/diagnostics13142317
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