Current trends and future orientation in diagnosing lung pathologies: A systematic survey

Abstract

Lung diseases pose a significant threat to public health worldwide, resulting in a substantial number of fatalities. Diseases such as chronic obstructive pulmonary disease and lung cancer constitute two of the three deadliest diseases worldwide, contributing to over 3 million deaths annually. This study offered a comparative analysis of different diagnostic techniques used for lung pathologies from an engineering standpoint. The review concentrated on intelligent detection methods, including electronic nose, computer vision (CV), or image processing, and biosensors such as graphene-field effect transistor (FET). The E-nose-based detection technique uses electronic sensors to recognize volatile organic compounds (VOCs) in the exhaled breath. These VOCs can aid in the diagnosis of lung pathologies such as pneumonia. The CV processing method involves the application of advanced imaging techniques and machine learning algorithms to scrutinize and diagnose lung pathologies and ventilator-associated pneumonia (VAP). Lastly, biosensors employ the exceptional properties of these materials to identify specific biomarkers in biological samples. This information can be used to diagnose lung pathologies and VAP. This study examined the current state-of-the-art methods and offers a comprehensive analysis of their advantages and disadvantages from an engineering perspective. The study underscored the potential of these techniques to enhance the diagnosis of lung pathologies and VAP and presents the advances in the field of smart biomedical applications. Additionally, it emphasized the necessity for further research to optimize their performance and clinical usefulness.