Diagnosing Lung Pathologies: A Systematic Survey, Current Trends, and Future Orientation

This study offers a comparative analysis of different diagnostic techniques for lung pathologies from an engineering standpoint. The review concentrates on three primary categories of methods: electronic nose-based (E-nose) detection, computer vision or image processing, and biosensors such as Graphene-FET (GFET). 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 computer vision 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. The present paper examines the current state-of-the-art in each methods and offers a comprehensive analysis of their advantages and disadvantages from an engineering perspective. The study underscores the potential of these techniques to enhance the diagnosis of lung pathologies and VAP. Additionally, it emphasizes the necessity for further research to optimize their performance and clinical usefulness.