Utility of E-Nose in Paediatric Respiratory Diseases Using Machine Learning Approach: A Pilot Study.

Abstract

To assess the feasibility of an electronic nose (e-nose) for volatile organic compounds (VOCs) to classify respiratory diseases and establish a protocol for use. We analysed exhaled air samples with Cyranose 320 nose from 67 children (14 healthy, 34 asthmatics and 19 other respiratory diseases) (mean age 11.4 years). Three samples were collected for each patient. We used machine learning models to generate an algorithm to classify children according to disease. There were significant differences in 30/32 sensors between plastic and tedlar bags. The Cronbach alpha was <0.5 in all cases, so that the consistency of the measurements was low. An analysis was performed to distinguish between healthy and asthmatics, P < .05 in 27/32 sensors. The algorithm predicted asthma disease with high sensitivity (over 99%) and accuracy (over 96%). The negative and positive predictive values were over 90%. The e-nose seems a valuable tool for screening asthma disease in the paediatric population in clinical practice.