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

• Published in: Diagnostics (Basel) Vol. 13; no. 14; p. 2317
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.07.2023; MDPI
• Subjects: Accuracy; Algorithms; Catheters; chest X-ray; Classification; Clinical medicine; Confidence intervals; Datasets; Deep learning; hidden stratification; Labeling; lines and tubes; machine learning; Neural networks; Patients; X-rays; Australia; deep learning; chest X-ray; machine learning; lines and tubes; hidden stratification
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics13142317

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.