Aritificial Inteligence Challenges in COPD management: a review

Abstract

Machine learning algorithms have been drawing attention in lung disease research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance. This study reviews the input parameters and the performance of machine learning applied to diagnosis of chronic obstructive pulmonary disease (COPD). One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 179, 1032, and 36,500 titles were identified from the PubMed, Scopus, and Google Scholar databases respectively. Studies that used machine learning to detect COPD and provided performance measures were included in our analysis. In the final analysis, 24 studies were included. The analysis of machine learning methods to detect COPD reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications. The performance of machine learning for diagnosis of COPD was considered satisfactory for several studies; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings.