RT Journal Article
SR Electronic
T1 Deep learning automates complete quality control of spirometric manoeuvre
JF European Respiratory Journal
JO Eur Respir J
FD European Respiratory Society
SP 3789
DO 10.1183/13993003.congress-2020.3789
VO 56
IS suppl 64
A1 Nilakash Das
A1 Kenneth Verstraete
A1 Marko Topalovic
A1 Jean-Marie Aerts
A1 Wim Janssens
YR 2020
UL http://erj.ersjournals.com/content/56/suppl_64/3789.abstract
AB 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.FootnotesCite 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).