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Deep learning automates complete quality control of spirometric manoeuvre

Nilakash Das, Kenneth Verstraete, Marko Topalovic, Jean-Marie Aerts, Wim Janssens
European Respiratory Journal 2020 56: 3789; DOI: 10.1183/13993003.congress-2020.3789
Nilakash Das
1Laboratory of Respiratory Diseases and Thoracic Surgery, Department of Chronic Diseases, Metabolism and Ageing, Katholieke Universiteit Leuven, Leuven, Belgium, Leuven, Belgium
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  • For correspondence: neel.das@kuleuven.be
Kenneth Verstraete
1Laboratory of Respiratory Diseases and Thoracic Surgery, Department of Chronic Diseases, Metabolism and Ageing, Katholieke Universiteit Leuven, Leuven, Belgium, Leuven, Belgium
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Marko Topalovic
2Laboratory of Respiratory Diseases and Thoracic Surgery, Department of Chronic Diseases, Metabolism and Ageing, Katholieke Universiteit Leuven, Leuven, Belgium, Artiq NV, Leuven, Belgium
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Jean-Marie Aerts
3Division Animal and Human Health Engineering, Department of Biosystems, Leuven, Belgium
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Wim Janssens
1Laboratory of Respiratory Diseases and Thoracic Surgery, Department of Chronic Diseases, Metabolism and Ageing, Katholieke Universiteit Leuven, Leuven, Belgium, Leuven, Belgium
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Abstract

Background: ATS/ERS spirometric acceptability criteria includes recommendations on forced expiration (FE) only. Unstable tidal breathing (TB) and insufficient forced inspiration (FI) also affects the validity of clinical indices like FVC. Further, these recommendations require visual inspection of spirograms, which introduces large inter-technician variability.

Objectives: We developed a deep learning model called convolutional neural network (CNN) to determine spirometry acceptability based on the complete manoeuvre that included TB, FI and FE.

Methods: Three experienced technicians independently labelled the acceptability of 1,325 complete manoeuvres using ATS/ERS 2005 protocols and a majority opinion established gold standard. We processed flow-volume loops into images, calculated ATS/ERS quantifiable criteria and developed a CNN on these features. Model development was done on 1000 curves by recalibrating a CNN, previously trained on 13,000 curves with acceptability labels from a junior technician, to predict the gold standard. We used the remaining 325 curves as a test-set.

Results: Around half of 1,325 manoeuvres met ATS/ERS acceptability. On testing (N=325), CNN demonstrated an accuracy of 87% with a high sensitivity (95%) and moderate specificity (80%). Finally, excluding 20% of manoeuvres of uncertain quality improved prediction accuracy (90[WJ1] %) and sensitivity (99%) but unchanged specificity.

Conclusion: Our model combines the visual experience of skilled technicians and ATS/ERS guidelines in automating spirometry acceptability based on entire manoeuvre. This can help in standardizing spirometry quality in different settings like clinical studies, laboratory testing and primary care practice.

  • Spirometry
  • Health policy
  • Telemedicine

Footnotes

Cite this article as: European Respiratory Journal 2020; 56: Suppl. 64, 3789.

This abstract was presented at the 2020 ERS International Congress, in session “Respiratory viruses in the "pre COVID-19" era”.

This is an ERS International Congress abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only).

  • Copyright ©the authors 2020
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Deep learning automates complete quality control of spirometric manoeuvre
Nilakash Das, Kenneth Verstraete, Marko Topalovic, Jean-Marie Aerts, Wim Janssens
European Respiratory Journal Sep 2020, 56 (suppl 64) 3789; DOI: 10.1183/13993003.congress-2020.3789

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Deep learning automates complete quality control of spirometric manoeuvre
Nilakash Das, Kenneth Verstraete, Marko Topalovic, Jean-Marie Aerts, Wim Janssens
European Respiratory Journal Sep 2020, 56 (suppl 64) 3789; DOI: 10.1183/13993003.congress-2020.3789
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