Journal Article

Machine learning-enabled diagnosis of viral respiratory infections from exhaled volatile organic compound analysis

Xu, Hao, Fu, Rong, Nie, Shuang, Zhang, Xiuya, Wu, Zheyang, Ni, Qianqian, Qin, Yingyi, Mo, Fengfeng

2026

Overview

Background The sensitivity and specificity of current breath biomarkers are often inadequate for effective requisite sensitivity for early-stage detection, thereby ignoring early-stage treatment in the patient. Methods In this study, we developed a screening model for viral respiratory infections using a combination of portable GC-MS and an artificial intelligence (AI) model. This platform employs machine learning algorithms to enhance the specificity and sensitivity of the model. Subsequently, we applied this platform to analyze 200 viral respiratory infections and normal exhaled samples. Results The diagnostic signatures, including 1-nonanethiol and 2-butanone, generated by the model effectively discriminated viral respiratory infection patients from normal controls with high sensitivity (90%), specificity (81%), and accuracy (AUC = 0.85). Furthermore, propionaldehyde and amylaldehyde, generated by the model, effectively discriminated COVID-19 from influenza A patients with sensitivity (87.5%), specificity (75%), and accuracy (AUC = 0.80). Data from UKBiobank indicated that in the volatile metabolite profiles exhaled by patients with viral respiratory infections, some characteristic components are related to the metabolic products of the host’s fatty acid β-oxidation pathway. Conclusion This study presents a diagnostic model that can identify novel and feasible breath biomarkers for detecting early-stage viral respiratory infections. The promising results position the platform as an efficient noninvasive screening test for clinical applications, offering potential advancements in early detection for viral respiratory infections.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Adult / Analysis / Artificial intelligence / Biomarkers / Biomarkers - analysis / Breath analysis / Breath tests / Breath Tests - methods / Butanones - analysis / Care and treatment / COVID-19 / Critical Care Medicine / Diagnosis / Exhalation / Female / Gas Chromatography-Mass Spectrometry / Health aspects / Humans / Influenza A / Influenza, Human - diagnosis / Intensive / Internal Medicine / Learning algorithms / Lung diseases / Machine Learning / Machine learning algorithms / Male / Medicine / Medicine & Public Health / Metabolites / Middle Aged / Patients / Pneumology/Respiratory System / Polymerase chain reaction / Propionaldehyde / Respiratory tract infection / Respiratory Tract Infections - diagnosis / Respiratory Tract Infections - metabolism / Respiratory Tract Infections - virology / Risk factors / Sensitivity and Specificity / Testing / UK Biobank / Viral infections / Viral respiratory infections / VOCs / Volatile organic compounds / Volatile organic compounds (VOCs) / Volatile Organic Compounds - analysis