Background: The forced oscillation technique (FOT) allows non-invasive lung function testing during quiet breathing even without expert guidance. However, it still relies on an operator for excluding breaths with artefacts such as swallowing, glottis closure and coughing. This manual selection is operator-dependent and time-consuming. We evaluated supervised machine learning methods to exclude breaths with artefacts from data analysis automatically. Methods: We collected 932 FOT measurements (Resmon Pro Full, Restech) from 155 patients (6–87 years) following the European Respiratory Society (ERS) technical standards. Patients were randomly assigned to either a training (70%) or test set. For each breath, we computed 71 features (including anthropometric, pressure stimulus, breathing pattern, and oscillometry data). Univariate filter, multivariate filter and wrapper methods for feature selection combined with several classification models were considered. Results: Trained operators identified 4333 breaths with- and 10244 without artefacts. Features selection performed by a wrapper method combined with an AdaBoost tree model provided the best performance metrics on the test set: Balanced Accuracy = 85%; Sensitivity = 79%; Specificity = 91%; AUC-ROC = 0.93. Differences in FOT parameters computed after manual or automatic breath selection was less than ∼0.25 cmH2O*s/L for 95% of cases. Conclusion: Supervised machine-learning techniques allow reliable artefact detection in FOT diagnostic tests. Automating this process is fundamental for enabling FOT for home monitoring, telemedicine, and point-of-care diagnostic applications and opens new scenarios for respiratory and community medicine.

Artificial intelligence for quality control of oscillometry measures

Veneroni C.;Dellaca' R.
2021

Abstract

Background: The forced oscillation technique (FOT) allows non-invasive lung function testing during quiet breathing even without expert guidance. However, it still relies on an operator for excluding breaths with artefacts such as swallowing, glottis closure and coughing. This manual selection is operator-dependent and time-consuming. We evaluated supervised machine learning methods to exclude breaths with artefacts from data analysis automatically. Methods: We collected 932 FOT measurements (Resmon Pro Full, Restech) from 155 patients (6–87 years) following the European Respiratory Society (ERS) technical standards. Patients were randomly assigned to either a training (70%) or test set. For each breath, we computed 71 features (including anthropometric, pressure stimulus, breathing pattern, and oscillometry data). Univariate filter, multivariate filter and wrapper methods for feature selection combined with several classification models were considered. Results: Trained operators identified 4333 breaths with- and 10244 without artefacts. Features selection performed by a wrapper method combined with an AdaBoost tree model provided the best performance metrics on the test set: Balanced Accuracy = 85%; Sensitivity = 79%; Specificity = 91%; AUC-ROC = 0.93. Differences in FOT parameters computed after manual or automatic breath selection was less than ∼0.25 cmH2O*s/L for 95% of cases. Conclusion: Supervised machine-learning techniques allow reliable artefact detection in FOT diagnostic tests. Automating this process is fundamental for enabling FOT for home monitoring, telemedicine, and point-of-care diagnostic applications and opens new scenarios for respiratory and community medicine.
Forced oscillation technique
Lung function
Machine learning
Measurement artefacts
Respiratory mechanics
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1185594
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