An Artificial Neural Network for Nasogastric Tube Position Decision Support

Ignat Drozdov; Rachael Dixon; Benjamin Szubert; Jessica Dunn; Darren Green; Nicola Hall; Arman Shirandami; Sofia Rosas; Ryan Grech; Srikanth Puttagunta; Mark Hall; David J Lowe

doi:10.1148/ryai.220165

An Artificial Neural Network for Nasogastric Tube Position Decision Support

Radiol Artif Intell. 2023 Feb 1;5(2):e220165. doi: 10.1148/ryai.220165. eCollection 2023 Mar.

Authors

Affiliation

¹ Bering Limited, 54 Portland Place, 2nd Floor, London W1B 1DY, England (I.D., R.D., B.S.); Emergency Department (J.D., D.G., N.H., A.S., S.R., D.J.L.) and Department of Radiology (R.G., S.P., M.H.), Queen Elizabeth University Hospital, Glasgow, Scotland; and Institute of Health and Wellbeing, University of Glasgow, Glasgow, Scotland (D.J.L.).

Abstract

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.Keywords: Neural Networks, Feature Detection, Supervised Learning, Machine Learning Supplemental material is available for this article. Published under a CC BY 4.0 license.

Keywords: Feature Detection; Machine Learning; Neural Networks; Supervised Learning.