Article Text

Original research
Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning study
  1. Robert Arntfield1,
  2. Blake VanBerlo2,
  3. Thamer Alaifan1,
  4. Nathan Phelps3,
  5. Matthew White1,
  6. Rushil Chaudhary4,
  7. Jordan Ho2,
  8. Derek Wu2
  1. 1Division of Critical Care Medicine, Western University, London, Ontario, Canada
  2. 2Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
  3. 3Department of Computer Science, Western University, London, Ontario, Canada
  4. 4Department of Medicine, Western University, London, Ontario, Canada
  1. Correspondence to Dr Robert Arntfield; robert.arntfield{at}gmail.com

Abstract

Objectives Lung ultrasound (LUS) is a portable, low-cost respiratory imaging tool but is challenged by user dependence and lack of diagnostic specificity. It is unknown whether the advantages of LUS implementation could be paired with deep learning (DL) techniques to match or exceed human-level, diagnostic specificity among similar appearing, pathological LUS images.

Design A convolutional neural network (CNN) was trained on LUS images with B lines of different aetiologies. CNN diagnostic performance, as validated using a 10% data holdback set, was compared with surveyed LUS-competent physicians.

Setting Two tertiary Canadian hospitals.

Participants 612 LUS videos (121 381 frames) of B lines from 243 distinct patients with either (1) COVID-19 (COVID), non-COVID acute respiratory distress syndrome (NCOVID) or (3) hydrostatic pulmonary edema (HPE).

Results The trained CNN performance on the independent dataset showed an ability to discriminate between COVID (area under the receiver operating characteristic curve (AUC) 1.0), NCOVID (AUC 0.934) and HPE (AUC 1.0) pathologies. This was significantly better than physician ability (AUCs of 0.697, 0.704, 0.967 for the COVID, NCOVID and HPE classes, respectively), p<0.01.

Conclusions A DL model can distinguish similar appearing LUS pathology, including COVID-19, that cannot be distinguished by humans. The performance gap between humans and the model suggests that subvisible biomarkers within ultrasound images could exist and multicentre research is merited.

  • chest imaging
  • ultrasound
  • respiratory infections
  • adult intensive & critical care
  • COVID-19
http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Supplementary materials

Footnotes

  • Twitter @arntfield

  • Contributors All authors were involved in the authorship of the manuscript, figures and tables. Overall project design and oversight (RA, BV), data management (TA, DW, RA, JH, MW), survey creation and distribution (MW), model training (BV, DW, NP), statistical analysis (NP), figure generation (JH, NP, BV) and literature search (RC, RA).

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available in a public, open access repository. Data are available upon reasonable request. The code for the neural network is available at the below URL. This code may be used to further this line of work. https://github.com/bvanberl/covid-us-ml Deidentified ultrasound images are not contained in an open repository due to the volume of data. Inquiries about this data may be directed to the corresponding author of this paper.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.