Identification of Multiple Pulmonary Diseases Using Volatile Organic Compounds Biomarkers in Human Exhaled Breath

Conditions studied

Lung Cancer, Lung Infection, COPD, Bronchitis, Pulmonary Fibrosis, Pulmonary Embolism, Pulmonary Arterial Hypertension, Pulmonary Tuberculosis, Pulmonary Abscess, Emphysema, Lung Injury, Cystic Fibrosis of the Lung, Bronchial Asthma, Bronchiectasis, Interstitial Lung Disease, Preserved Ratio Impaired Spirometry

About this study

The goal of this observational study is to develop an advanced expiratory algorithm model utilizing exhaled breath volatile organic compound (VOC) marker molecules. This model aims to accurately diagnose mutiple pulmonary diseases. The primary objectives it strives to accomplish are: 1. To assess the diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in diagnose several common pulmonary diseases. 2. To assess the diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in diagnose more pulmonary diseases.

Interventions

• Other: Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system — Exhaled breath samples from these participants will be collected and analyzed to detect volatile organic compound molecules in human exhaled breath by GC-MS and μGC-PID

Primary outcomes

• The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in the diagnosis of several common pulmonary diseases. (2 years)

Eligibility information

Study locations

• The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510140 China