Evidence map›Paper›PMID 37035435›Full record

ArticleRadiology. Artificial intelligence2023

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 and 2 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.9field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 17 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 1 institution in 1 country.

Ignat DrozdovBering 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.).ORCID 0000-0001-6727-4688
Rachael DixonBering 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.).ORCID 0000-0003-0130-7023
Benjamin SzubertBering 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.).ORCID 0000-0001-6624-409X
Jessica DunnBering 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.).
Darren GreenBering 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.).ORCID 0000-0001-8063-6671
Nicola HallBering 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.).ORCID 0000-0001-9069-527X
Arman ShirandamiBering 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.).
Sofia RosasBering 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.).ORCID 0000-0002-1333-5307
Ryan GrechBering 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.).ORCID 0000-0002-2593-7030
Srikanth PuttaguntaBering 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.).
Mark HallBering 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.).ORCID 0000-0002-6310-0872
David J LoweBering 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.).ORCID 0000-0003-4866-2049
Queen Elizabeth University Hospital · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Feature DetectionMachine LearningNeural NetworksSupervised Learning

Identifiers

PMID37035435
PMCPMC10077078
OpenAlexW4318823572

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.