Deep Learning–Based Localization and Detection of Malpositioned Nasogastric Tubes on Portable Supine Chest X-Rays in Intensive Care and Emergency Medicine: A Multi-center Retrospective Study
Journal
Journal of Imaging Informatics in Medicine
Journal Volume
38
Journal Issue
1
Start Page
335
End Page
345
ISSN
2948-2933
Date Issued
2024-07-09
Author(s)
Hwang, Tianyu
Roth, Holger R.
Yang, Dong
Zhao, Can
Abstract
Malposition of a nasogastric tube (NGT) can lead to severe complications. We aimed to develop a computer-aided detection (CAD) system to localize NGTs and detect NGT malposition on portable chest X-rays (CXRs). A total of 7378 portable CXRs were retrospectively retrieved from two hospitals between 2015 and 2020. All CXRs were annotated with pixel-level labels for NGT localization and image-level labels for NGT presence and malposition. In the CAD system, DeepLabv3 + with backbone ResNeSt50 and DenseNet121 served as the model architecture for segmentation and classification models, respectively. The CAD system was tested on images from chronologically different datasets (National Taiwan University Hospital (National Taiwan University Hospital)-20), geographically different datasets (National Taiwan University Hospital-Yunlin Branch (YB)), and the public CLiP dataset. For the segmentation model, the Dice coefficients indicated accurate delineation of the NGT course (National Taiwan University Hospital-20: 0.665, 95% confidence interval (CI) 0.630–0.696; National Taiwan University Hospital-Yunlin Branch: 0.646, 95% CI 0.614–0.678). The distance between the predicted and ground-truth NGT tips suggested accurate tip localization (National Taiwan University Hospital-20: 1.64 cm, 95% CI 0.99–2.41; National Taiwan University Hospital-Yunlin Branch: 2.83 cm, 95% CI 1.94–3.76). For the classification model, NGT presence was detected with high accuracy (area under the receiver operating characteristic curve (AUC): National Taiwan University Hospital-20: 0.998, 95% CI 0.995–1.000; National Taiwan University Hospital-Yunlin Branch: 0.998, 95% CI 0.995–1.000; CLiP dataset: 0.991, 95% CI 0.990–0.992). The CAD system also detected NGT malposition with high accuracy (AUC: National Taiwan University Hospital-20: 0.964, 95% CI 0.917–1.000; National Taiwan University Hospital-Yunlin Branch: 0.991, 95% CI 0.970–1.000) and detected abnormal nasoenteric tube positions with favorable performance (AUC: 0.839, 95% CI 0.807–0.869). The CAD system accurately localized NGTs and detected NGT malposition, demonstrating excellent potential for external generalizability.
SDGs
Publisher
Springer Nature
Type
journal article