Computer-aided classification of small airways dysfunction using impulse oscillometric features: a children-focused review

Abstract

Abstract

Background and objective Spirometry, which is the most commonly used technique for asthma diagnosis, is often unsuitable for small children as it requires them to follow exact instructions and perform extreme inspiration and expiration maneuvers. In contrast, impulse oscillometry (IOS) is a child-friendly technique that could serve as an alternative pulmonary function test (PFT) for asthma diagnosis and control in children as it offers several advantages over spirometry. However, the complex test results of IOS may be difficult to be understood by practitioners due to its reliance on mechanical and electrical models of the human pulmonary system. Recognizing this reality, computer-aided decision systems could help to improve the utility of IOS. The main objective of this paper is to understand the current computer-aided classification research works on this topic.

Methods This paper presents a methodological review of research works related to the computer-aided classification of peripheral airway obstruction using the IOS technique, which is focused on, but not limited to, asthmatic children. Publications that focused on computer-aided classification of asthma, peripheral dysfunction and/or small airway impairment (SAI) based on impulse oscillometric features were selected for this review.

Results Out of the 34 articles that were identified using the selected scientific web databases and topic-related parameters, only eight met the eligibility criteria. The most relevant results of the articles reviewed are related to the performance of the different classifiers using static features which are solely based on the first pulmonary function testing measurements (IOS and spirometry). These results included an overall classifiers’ accuracy performance ranging from 42.24% to 98.61%.

Conclusion There is still a great opportunity to improve the utility of IOS by developing more computer-aided robust classifiers, specifically for the asthmatic children population as the classification studies performed to date (1) are limited in number, (2) include features derived from tests that are not optimally suitable for children, (3) are solely bi-class (mostly asthma and non-asthma) and therefore fail to include different degrees of peripheral obstruction for disease prevention and control and (4) lack of validation in cases that focus on multi-class classification of the different degrees of peripheral airway obstruction.