An artificial neural network for nasogastric tube position decision support

Drozdov, l. et al. (2023) An artificial neural network for nasogastric tube position decision support. Radiology: Artificial Intelligence, 5(2), e220165. (doi: 10.1148/ryai.220165)

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Purpose: To develop and validate a deep learning model for detection of nasogastric tube (NGT) malposition on chest radiographs and assess model impact as a clinical decision support tool for junior physicians to help determine whether feeding can be safely performed in patients (feed/do not feed). Materials and Methods: A neural network ensemble was pretrained on 1 132 142 retrospectively collected (June 2007–August 2019) frontal chest radiographs and further fine-tuned on 7081 chest radiographs labeled by three radiologists. Clinical relevance was assessed on an independent set of 335 images. Five junior emergency medicine physicians assessed chest radiographs and made feed/do not feed decisions without and with artificial intelligence (AI)-generated NGT malposition probabilities placed above chest radiographs. Decisions from the radiologists served as ground truths. Model performance was evaluated using receiver operating characteristic analysis. Agreement between junior physician and radiologist decision was determined using the Cohen κ coefficient. Results: In the testing set, the ensemble achieved area under the receiver operating characteristic curve values of 0.82 (95% CI: 0.78, 0.86), 0.77 (95% CI: 0.71, 0.83), and 0.98 (95% CI: 0.96, 1.00) for satisfactory, malpositioned, and bronchial positions, respectively. In the clinical evaluation set, mean interreader agreement for feed/do not feed decisions among junior physicians was 0.65 ± 0.03 (SD) and 0.77 ± 0.13 without and with AI support, respectively. Mean agreement between junior physicians and radiologists was 0.53 ± 0.05 (unaided) and 0.65 ± 0.09 (AI-aided). Conclusion: A simple classifier for NGT malposition may help junior physicians determine the safety of feeding in patients with NGTs.

Item Type:Articles
Additional Information:This work was supported by Bering Limited and the Industrial Centre for AI Research in Digital Diagnostics (iCAIRD) which is funded by the Data to Early Diagnosis and Precision Medicine strand of the government’s Industrial Strategy Challenge Fund, managed and delivered by Innovate UK on behalf of UK Research and Innovation (UKRI) (project no. 104690).
Glasgow Author(s) Enlighten ID:Lowe, Dr David
Authors: Drozdov, l., Dixon, R., Szubert, B., Dunn, J., Green, D., Hall, N., Shirandami, A., Rosas, S., Grech, R., Puttagunta, S., Hall, M., and Lowe, D. J.
College/School:College of Medical Veterinary and Life Sciences > School of Medicine, Dentistry & Nursing
Journal Name:Radiology: Artificial Intelligence
Publisher:Radiological Society of North America
ISSN (Online):2638-6100
Published Online:01 February 2023
Copyright Holders:Copyright © 2023 The Authors
First Published:First published in Radiology: Artificial Intelligence 5(2): e220165
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
304546I-CAIRD: Industrial Centre for AI Research in Digital DiagnosticsKeith MuirInnovate UK (INNOVATE)104690Stroke & Brain Imaging