Deep learning algorithm helps to standardize ATS/ERS spirometric acceptability and usability criteria

Read the official abstract here.

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 standardize 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=3,738), 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<1s (MEFVC pattern within first second of exhalation) and plateau in volume-time were most important in determining acceptability, while MEFVC<1s entirely determined usability.

Conclusion: The CNNs identified relevant attributes in spirometric curves to standardize 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.