Non-invasive Approach for Disease Diagnosis

Abstract

The human respiration involves inhalation and exhalation of atmospheric air. The exhaled breath contains around 3000 organic compounds (VOCs) which are linked to human metabolic and biochemical processes occurring inside the body. The electronic nose plays a vital role in identification of various diseases by breath sample signature analysis. Electronic nose is a non-invasive technology for disease monitoring with systematic integration of sensor array and artificial intelligence. The blood glucose monitoring methods currently used in medical field are invasive and uncomfortable to patients due to needle pricking for blood sample collection. Due to which the need for non-invasive and alternative screening/diagnostic technologies is anticipated. The lung cancer is globally one of the cancer types with high fatal ratio. The diagnostic techniques like sputum cytology, radiology and tomography are not accessible to worldwide population for screening. The non-invasive techniques like mass spectrometry and gas chromatography require skilled technicians and are not portable and economical for wide range of population. The exhaled human breath contains biomarkers for several diseases so non-invasive methods will definitely be advantageous over the existing invasive and expensive methods. The review briefs the modern collaborative machine-learning approach with non-invasive, economical and easy to use framework for disease detection and diagnosis.