TY - JOUR T1 - Deep learning algorithm helps to standardise ATS/ERS spirometric acceptability and usability criteria JF - European Respiratory Journal JO - Eur Respir J DO - 10.1183/13993003.00603-2020 SP - 2000603 AU - Nilakash Das AU - Kenneth Verstraete AU - Sanja Stanojevic AU - Marko Topalovic AU - Jean-Marie Aerts AU - Wim Janssens Y1 - 2020/01/01 UR - http://erj.ersjournals.com/content/early/2020/06/08/13993003.00603-2020.abstract N2 - Rationale While ATS/ERS quality control criteria for spirometry include several quantitative limits, it also requires manual visual inspection. The current approach is time consuming and leads to high inter-technician variability. We propose a deep learning approach called convolutional neural network (CNN), to standardise spirometric manoeuvre acceptability and usability.Methods and methods In 36 873 curves from the national health and nutritional examination survey (NHANES) USA 2011–12, technicians labelled 54% of curves as meeting ATS/ERS 2005 acceptability criteria with satisfactory start and end of test but identified 93% of curves with a usable FEV1. We processed raw data into images of maximal expiratory flow-volume curve (MEFVC), calculated ATS/ERS quantifiable criteria, and developed CNNs to determine manoeuvre acceptability and usability on 90% of the curves. The models were tested on the remaining 10% of curves. We calculated Shapley values to interpret the models.Results In the test set (N=3738), CNN showed an accuracy of 87% for acceptability and 92% for usability, with the latter demonstrating a high sensitivity (92%) and specificity (96%). They were significantly superior (p<0.0001) to ATS/ERS quantifiable rule-based models. Shapley interpretation revealed MEFVC<1 s (MEFVC pattern within first second of exhalation) and plateau in volume-time were most important in determining acceptability, while MEFVC<1 s entirely determined usability.Conclusion The CNNs identified relevant attributes in spirometric curves to standardise ATS/ERS manoeuvre acceptability and usability recommendations, and further provides individual manoeuvre feedback. Our algorithm combines the visual experience of skilled technicians and ATS/ERS quantitative rules in automating the critical phase of spirometry quality control.FootnotesThis manuscript has recently been accepted for publication in the European Respiratory Journal. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article.Conflict of interest: Dr. Das reports has a patent Spirometry evaluation method (Application no. 1914446.8 UK patent office) pending.Conflict of interest: Dr. Verstraete has nothing to disclose.Conflict of interest: Dr. Stanojevic has nothing to disclose.Conflict of interest: Dr. Topalovic reports to be a co-founder of a spin-off company ArtiQ.Conflict of interest: Dr. Aerts has not nothing to disclose.Conflict of interest: Dr. Janssens reports grants from AstraZeneca, Chiesi, fees for lectures and advisory boards from AstraZenca, Chiesi, outside the submitted work; In addition, Dr. Janssens has a patent on ' on pulmonary function loops' pending and Wim Janssens is co-founder of ArtiQ, a spin-off of KU Leuven. ER -