Utility of E-Nose in Paediatric Respiratory Diseases Using Machine Learning Approach: A Pilot Study
Ana Díez-Izquierdo1,2, Hipólito Perez Martín2, Cristina Lidón Moyano2
1Pediatric Pulmonology and Allergology Section, Department of Pediatrics, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Spain.
Insights
An electronic nose (e-nose) shows promise for screening pediatric asthma by analyzing volatile organic compounds (VOCs) in breath. This technology achieved high accuracy in classifying asthma in children.
Area of Science:
- Pulmonary Medicine
- Biomedical Engineering
- Analytical Chemistry
Background:
- Respiratory diseases in children pose a significant health burden.
- Accurate and early diagnosis is crucial for effective management.
- Current diagnostic methods can be invasive or complex.
Purpose of the Study:
- To evaluate the feasibility of using an electronic nose (e-nose) to detect volatile organic compounds (VOCs) in exhaled breath.
- To develop a protocol for classifying pediatric respiratory diseases using e-nose technology.
- To assess the e-nose's potential for screening asthma in children.
Main Methods:
- Exhaled air samples were collected from 67 children (14 healthy, 34 asthmatics, 19 with other respiratory diseases) using the Cyranose 320 e-nose.
- Machine learning models were employed to create a classification algorithm.
- Sensor data differences between plastic and Tedlar bags were analyzed, alongside measurement consistency (Cronbach's alpha).
Main Results:
- Significant differences (P < .05) were observed in 27 out of 32 sensors when distinguishing between healthy children and asthmatics.
- The developed algorithm demonstrated high sensitivity (over 99%) and accuracy (over 96%) in predicting asthma.
- Negative and positive predictive values exceeded 90%.
Conclusions:
- The e-nose is a potentially valuable tool for screening asthma in the pediatric population.
- The study established a protocol for e-nose use in classifying respiratory diseases.
- Further validation may enhance its clinical application for early asthma detection.
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.
