Abstract
Coughing is a common symptom of several respiratory diseases. The sound and type of cough are useful features to consider when diagnosing a disease. Respiratory infections pose a significant risk to human lives worldwide as well as a significant economic downturn, particularly in countries with limited therapeutic resources. In this study we reviewed the latest proposed technologies that were used to control the impact of respiratory diseases. Artificial Intelligence (AI) is a promising technology that aids in data analysis and prediction of results, thereby ensuring peopleâs well-being. We conveyed that the cough symptom can be reliably used by AI algorithms to detect and diagnose different types of known diseases including pneumonia, pulmonary edema, asthma, tuberculosis (TB), COVID19, pertussis, and other respiratory diseases. We also identified different techniques that produced the best results for diagnosing respiratory disease using cough samples. This study presents the most recent challenges, solutions, and opportunities in respiratory disease detection and diagnosis, allowing practitioners and researchers to develop better techniques.
Highlights
Lung malfunctioning poses an increased mortality and morbidity risk on the global population
Other techniques were proposed which are not related to machine learning such as Wavelet-based crackle detection/Morlet and Du wavelets, signal processing techniques, prediction model (LASSO-penalized logistic regression), Fractional exhaled nitric oxide (FeNO) measurements and an airway responsiveness test, cough embeddings cosine and Polymerase Chain Reaction (PCR) but those techniques were used to diagnose and detect respiratory diseases, as shown in the Figures 3,4 and 5
We presented some challenges and opportunities that regulators and developers can use to enhance the effectiveness and quality of the proposed artificial intelligence (AI)-based solutions. van Vugt et al [79] discussed that the core challenge is the heterogeneity of the dataset, the lack of tools to test the feasibility of deep learning models, unbalanced data in the dataset, predicted or null data in the dataset, the generality of the machine learning model, a larger number of negative images than positive images which causes an error in prognostication, and the reliability of AI hardware and software
Summary
Lung malfunctioning poses an increased mortality and morbidity risk on the global population. The risk is elevated in developing counties that experience increased pollution due to many factories and lack of efficient air ventilation solutions. Bronchitis, pertussis and COVID-19 share coughing as a common symptom. The cough sound tends to be unique for each respiratory disease enabling physicians to diagnose the illness from the cough sound itself. The healthcare system is engaging more with AI to help doctors in predicting and diagnosing a variety of diseases [2], especially in the past year when the COVID-19 virus became a pandemic and there were not enough hospitals to provide a proper service to the patients [3]. Due to the fatal consequences of respiratory diseases, it is important to develop cost effective and convenient technologies to control them. According to the World Health Organization (WHO), healthcare technologies manifest great contribution in improving
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