Keywords
exhaled breath aerosol; breath condensate; pathogen detection; respiratory infection; non-sputum
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Jain S, Baarends MM, Grilli M et al. Breath-Based Pathogen Detection Technologies for Diagnosing Respiratory Infections: A Scoping Review Protocol [version 1; peer review: awaiting peer review]. F1000Research 2026, 15:320 (https://doi.org/10.12688/f1000research.177363.1)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
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Study Protocol
Breath-Based Pathogen Detection Technologies for Diagnosing Respiratory Infections: A Scoping Review Protocol
[version 1; peer review: awaiting peer review]
PUBLISHED 25 Feb 2026
OPEN PEER REVIEW
REVIEWER STATUS
Abstract
Corresponding author:
Sonal Jain
Competing interests:
No competing interests were disclosed.
Grant information:
This systematic scoping review is funded by the National Institutes of Health (NIH) (funding reference number U01AI152087; Rapid Research in Diagnostics Development for Tuberculosis Network), HORIZON Research and Innovation Actions (HORIZON-RIA; HORIZON-HLTH-2022-DISEASE-07-02) Project 101094804 - BreathForDx, and HORIZON Innovation Actions (HORIZON-IA; HORIZON-HLTH-2023-TOOL-05-08) Project 101136380 - B-Path. The funders did not have a role in developing the protocol. AGW is supported by the UK National Institute for Health and Care Research (NIHR305136) and in part by the NIHR Imperial Biomedical Research Centre (BRC) and NIHR HealthTech Research Centre (HRC) in In Vitro Diagnostics.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Copyright:
© 2026 Jain S et al.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite: Jain S, Baarends MM, Grilli M et al. Breath-Based Pathogen Detection Technologies for Diagnosing Respiratory Infections: A Scoping Review Protocol [version 1; peer review: awaiting peer review]. F1000Research 2026, 15:320 (https://doi.org/10.12688/f1000research.177363.1)
First published: 25 Feb 2026, 15:320 (https://doi.org/10.12688/f1000research.177363.1)
Latest published: 25 Feb 2026, 15:320 (https://doi.org/10.12688/f1000research.177363.1)
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Strengths and limitations of this study
• To our knowledge, this is the first scoping review to specifically map XBA and EBC-based capture and detection systems for respiratory pathogen identification.
• The review includes studies that have reported data with contrived and/or clinical samples, focusing on clinically relevant technologies.
• We will adhere to PRISMA-ScR guidelines, ensuring transparency, reproducibility, and systematic reporting of findings.
• Data will be extracted by a single reviewer. However, the extracted data will be verified by an additional reviewer.
• Grey literature will not be searched for identifying new technologies, which may result in exclusion of novel approaches still under development. However, given our focus on technologies that are closer to clinical use, such sources are unlikely to provide sufficient information on clinical performance.
1. Introduction
Respiratory pathogens are among the leading causes of morbidity and mortality,1 placing a significant burden on communities and health systems worldwide.2 The clinical presentations of respiratory infections are often non-specific, making rapid and accurate diagnostics essential for identifying underlying pathogens and optimizing treatment regimens. Furthermore, with the rise of antimicrobial resistance (AMR), respiratory infections now account for the highest number of deaths globally due to ineffective or delayed treatment.3 The COVID-19 pandemic has further underscored the critical role of diagnostics in disease surveillance, screening, and informing treatment strategies, all of which are essential for guiding public health interventions.4 However, despite advancement in diagnostic technologies, major challenges remain in the accessibility, accuracy, and feasibility of current testing methods, particularly in resource-constrained settings and in diagnosing diverse patient groups including children, elderly, individuals with co-morbidities, and those with subclinical disease. As a result, most respiratory infections go untested.
One of the key limitations in respiratory disease diagnostics lies in sample collection methods. Sputum, while a commonly used specimen type, presents several challenges. Certain demographics, such as children, severely ill individuals, and those living with HIV, often struggle to produce adequate sputum samples.5,6 Moreover, sputum collection poses biohazard risks to healthcare workers, introduces sample variability, and is difficult to process due to its viscous nature.7,8 For tuberculosis (TB), more than one-third of cases go undiagnosed every year, partly because testing relies primarily on sputum.9 Swab sampling of the nose, throat, or nasopharyngeal tract is often uncomfortable for patients and may not capture lower respiratory tract infections (LTRI).10 Both swab and sputum samples are frequently contaminated with commensal bacteria from the upper respiratory tract,11 making it difficult to distinguish colonization from true infection using microbiological methods. This presents a significant challenge in the diagnosis of LRTI (especially in hospital or ventilator associated pneumonia), as well as in the monitoring and treatment of common pulmonary diseases such as bronchiectasis, chronic obstructive pulmonary disease (COPD), and cystic fibrosis (CF), where infections can trigger disease exacerbations but have to be distinguished from colonization. If the causative pathogen is unknown, clinicians rely on syndromic management and empirical treatment, which may fail to cover atypical or resistant organisms, or may lead to overtreatment.12 Moreover, inappropriate empirical treatment increases the risk of AMR, leading to both individual harm and broader public health consequences of transmission. Advancements in sensitive, multiplex molecular platforms have enhanced the ability to detect respiratory pathogens, identify AMR genes, and distinguish between viral and bacterial infections, reducing unnecessary antibiotic use.13 Expanding the use of these technologies with non-sputum sampling methods could transform respiratory disease detection.
Aerosol transmission of respiratory infections has been historically underestimated, with the COVID-19 pandemic challenging the traditional views on droplet and fomite transmission.14,15 Robust evidence now confirms that aerosols generated in exhaled breath play a major role in the transmission of several respiratory infections including pandemic coronaviruses, influenza, respiratory syncytial virus (RSV), and TB.16–20 This growing understanding of aerosol transmission has driven researchers to explore exhaled breath as a viable sample type for detecting respiratory infections. Exhaled breath carries pathogen-containing aerosols and organic materials, making it a rich source of biomarkers for disease detection.21,22 The COVID-19 pandemic has further accelerated innovation in simplified exhaled breath collection devices, leveraging advances in aerobiology, bioengineering, and material science for improved pathogen capture and detection. Exhaled breath has thus emerged as a promising, non-invasive sample type for detecting upper and lower respiratory tract infections, particularly in patients unable to produce sputum.
Many breath-based diagnostics have focused on the analysis of infection-associated metabolites, specifically volatile organic compounds (VOCs), as indicators of disease or host response. However, their specificity for diagnosing respiratory infections is often limited as similar metabolic changes in the body can be triggered by various diseases and clinical utility will depend substantially on prevalence.23 In contrast, direct pathogen capture from exhaled breath aerosol (XBA) and exhaled breath condensate (EBC), coupled with molecular, antigen-based, or microbiological detection methods, offers a more specific approach, which is of particular interest for clinical diagnosis. Furthermore, direct pathogen capture and ideally quantitation can be linked to infectiousness of individuals.24 This provides a unique opportunity to correlate diagnostic results with transmission.25 This capability is particularly valuable for public health control, as it allows for targeted interventions and supports disease control in community and healthcare settings. The landscape of breath-based capture and detection technologies is rapidly expanding, driven by technological advancements and a growing interest in non-invasive, user-friendly sampling methods. As the field matures, it is increasingly important to map the breadth of current innovations with clinical potential, identify use cases, key technological gaps, and assess their diagnostic accuracy across various respiratory pathogens and clinical syndromes. By identifying existing research and technological gaps, this review aims to guide future research and development, supporting integration of breath-based diagnostics into diverse healthcare settings.
2. Methods and analysis
2.1 Objectives
This scoping review aims to systematically map the current landscape of breath-based technologies for direct pathogen capture and/or detection, detailing their underlying methodologies, diagnostic performance, strengths, limitations, and potential for clinical adoption. Specifically, we will characterize the breath sample collection systems (e.g. face masks, blow tubes, mouth pieces), sampling methods, detection methods, diagnostic accuracy, and the target disease(s) or use case(s). The review will present the strengths and limitations of these technologies, identify knowledge gaps, and highlight key areas for future research. The insights gained will inform ongoing research and development efforts for advancing diagnostic capabilities for high-burden respiratory infections.
2.2 Overview
This protocol is developed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) guidelines.26 The completed checklist is available under Supplementary Information. This reporting of findings will adhere to the PRISMA extension Scoping Reviews (PRISMA-ScR) checklist.27
2.3 Definitions
• Exhaled breath aerosol (XBA): Respiratory aerosols ≤5 μm in diameter that result from evaporation of droplets exhaled, coughed or sneezed into the atmosphere, or by aerosolization of infective material present in exhaled breath.
• Exhaled breath condensate (EBC): A biological sample collected by cooling exhaled breath, causing the water vapor and aerosols to condense into liquid. This process facilitates the transition of aerosols into the liquid phase, enabling for downstream analysis.
2.4 Eligibility criteria
We will include cohort studies, cross-sectional studies, randomized-controlled trials, case-control studies, case series, and observational studies published between 1 January 2015 and 19 February 2025. We will also include case reports, narratives, commentaries, editorials, letters, and reviews. For reviews, we will screen the reference lists to identify citations that were not captured in our primary search. We will include pre-prints that appear in our database searches. Publications reporting on breath-based technologies that meet the following criteria will be included, with no restriction on language:
• Pre-clinical or clinical studies utilizing XBA/EBC as the primary sample type for direct pathogen capture and/or detection.
• Studies reporting original data on analytical and/or clinical diagnostic performance from contrived (spiked) and/or clinical samples, with no restriction on study population and size.
• Studies aimed at diagnosing any respiratory infections including but not limited to pandemic coronaviruses, influenza, RSV, TB, and COPD.
We will exclude publications that meet any of the following conditions:
• Early-stage analytical studies and proof-of-concept studies that utilize XBA and/or EBC but do not report data from contrived and/or clinical samples.
• Focus solely on metabolite and VOC-based detection for respiratory infections or diseases such as cancer, metabolic disorders, gastrointestinal conditions, which do not relate to respiratory disorders.
• Exclusively assess pathogen detection through non-breath samples such as oral swab, blood, sputum, urine, or stool.
• Primarily investigate host biomarkers, host immune responses, or general health indicators in breath without directly detecting infectious pathogens.
• Focus on research relating to air quality, pollution detection, or other environmental applications of air sampling that are not aimed at diagnosing respiratory infections.
• Report data exclusively in animal models or non-human subjects.
2.5 Information sources
A systematic search will be conducted through electronic bibliographic databases including MEDLINE (via PubMed), EMBASE, and Web of Science for peer-reviewed literature and pre-prints. However, preprint servers will not be searched separately. Grey literature sources will be searched to obtain supplementary or missing information on technologies identified in the included studies. The exact search strategy for grey literature will be adapted as needed for each identified technology based on the specific information required, with relevant sources selected to address information gaps or to provide further insights. The following grey literature sources will be utilized:
• Clinical trial registries
ii. WHOTrials ICTRP (https://trialsearch.who.int/)29
• Websites of diagnostic test databases and news outlets
i. FIND (https://www.finddx.org/tools-and-resources/dxconnect/test-directory/)30
ii. 360Dx: Clinical Diagnostics Technology and Disease Research News (https://www.360dx.com/)31
• Developer websites
• Patents
2.6 Search strategy
The search strategy was developed with a medical librarian using terms shown in Table 1.
Table 1. Search strategy.
2.7 Study records
Covidence will be used to organize and screen all identified articles, as well as remove duplicates. Two reviewers will independently screen all titles and abstracts for eligibility. Then, full-text screening will be conducted by the same reviewers. Any conflicts will be resolved by consensus or discussion with a third reviewer.
2.8 Data collection process
Data extraction will be conducted manually using a standardized form created in Covidence. The form will be piloted using a subset of eligible publications before being finalized for use with the full set of included studies. During this process, fields will be iteratively added or removed to ensure flexibility in data collection. Data will be collected on device description, breath collection methodology, detection methodology, diagnostic performance, study details, and operational features ( Table 2). Initial data extraction will be performed by a single reviewer, followed by verification by a second reviewer. Any conflicts will be resolved by consensus or discussion with a third reviewer. Missing data will be addressed by reaching out to the authors, or by searching grey literature sources.
Table 2. Data extraction strategy.
2.9 Risk of bias in individual studies
This scoping review aims at providing an overview of breath-based diagnostic technologies using XBA and EBC. Therefore, a risk of bias assessment will not be included.
2.10 Data synthesis
Data will be presented in tabular form and organized by key themes such as device details, sample collection methodology, diagnostic performance, target application, and operational features. A narrative summary will accompany the table to highlight key trends, discuss strengths and weaknesses, and potential areas for further research.
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how to cite this article
Jain S, Baarends MM, Grilli M et al. Breath-Based Pathogen Detection Technologies for Diagnosing Respiratory Infections: A Scoping Review Protocol [version 1; peer review: awaiting peer review]. F1000Research 2026, 15:320 (https://doi.org/10.12688/f1000research.177363.1)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
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