{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T23:41:44Z","timestamp":1783035704185,"version":"3.54.6"},"reference-count":153,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"publisher","award":["101115442"],"award-info":[{"award-number":["101115442"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007352","name":"State Secretariat for Education, Research, and Innovation","doi-asserted-by":"crossref","award":["REF-1131-52304"],"award-info":[{"award-number":["REF-1131-52304"]}],"id":[{"id":"10.13039\/501100007352","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100007352","name":"State Secretariat for Education, Research, and Innovation","doi-asserted-by":"crossref","award":["SBFI-Nr.23.00369"],"award-info":[{"award-number":["SBFI-Nr.23.00369"]}],"id":[{"id":"10.13039\/501100007352","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Breath analysis is a non-invasive diagnostic method that offers insights into both physiological and pathological conditions. Exhaled breath contains volatile organic compounds, which act as biomarkers for disease detection, allowing for the monitoring of treatments and the tailoring of medicine to individuals. Recent advancements in chemical sensing, mass spectrometry, and spectroscopy have improved the ability to identify these biomarkers; however, traditional statistical approaches often struggle to handle the complexities of breath data. Artificial intelligence (AI) and machine learning (ML) have revolutionized breath analysis by uncovering intricate patterns among volatile breath markers, enhancing diagnostic precision, and facilitating real-time disease identification. Despite significant progress, challenges remain, including issues with data standardization, model interpretability, and the necessity for extensive and varied datasets. This study reviews the applications of ML in analyzing breath volatile organic compounds, highlighting methodological shortcomings and obstacles to clinical validation. A thorough literature review was performed using the PubMed and Scopus databases, which included studies that focused specifically on the role of machine learning in disease diagnosis and incidence prediction via breath analysis. Among the 524 articles reviewed, 97 satisfied the specified inclusion criteria. The selected studies applied ML techniques, fell within the scope of this review, and emphasize the potential of ML models for non-invasive diagnostics. The findings indicate that traditional ML methods dominate, while ensemble methods are on the rise, and deep learning (DL) techniques (especially CNNs and LSTMs) are increasingly used for classifying respiratory diseases. Techniques for feature selection (such as PCA and ML-based methods) were frequently implemented, though challenges related to explainability and data standardization persist. Future studies should focus on enhancing model transparency and developing methods to further integrate AI into the clinical setting to facilitate early disease detection and advance precision medicine.<\/jats:p>","DOI":"10.3390\/info16110968","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T17:45:34Z","timestamp":1762796734000},"page":"968","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Artificial Intelligence and Machine Learning in the Diagnosis and Prognosis of Diseases Through Breath Analysis: A Scoping Review"],"prefix":"10.3390","volume":"16","author":[{"given":"Christos","family":"Kokkotis","sequence":"first","affiliation":[{"name":"AIDEAS O\u00dc, 10117 Tallinn, Estonia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1090-2177","authenticated-orcid":false,"given":"Serafeim","family":"Moustakidis","sequence":"additional","affiliation":[{"name":"AIDEAS O\u00dc, 10117 Tallinn, Estonia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9120-302X","authenticated-orcid":false,"given":"Stefan James","family":"Swift","sequence":"additional","affiliation":[{"name":"Department of Chemistry and Applied Biosciences, ETHZ, 8093 Z\u00fcrich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3550-8536","authenticated-orcid":false,"given":"Flora","family":"Kontopidou","sequence":"additional","affiliation":[{"name":"Mitera Childrens\u2019 Hospital, 15123 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0448-9459","authenticated-orcid":false,"given":"Ioannis","family":"Kavouras","sequence":"additional","affiliation":[{"name":"Institute of Communications and Computer Systems, Iroon Polytechniou 9, 15773 Zografou, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anastasios","family":"Doulamis","sequence":"additional","affiliation":[{"name":"Institute of Communications and Computer Systems, Iroon Polytechniou 9, 15773 Zografou, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5066-533X","authenticated-orcid":false,"given":"Stamatios","family":"Giannoukos","sequence":"additional","affiliation":[{"name":"Department of Chemistry and Applied Biosciences, ETHZ, 8093 Z\u00fcrich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2050","DOI":"10.2174\/1381612823666170130155627","article-title":"Metabolomics in the Diagnosis and Pharmacotherapy of Lung Diseases","volume":"23","author":"Devillier","year":"2017","journal-title":"Curr. Pharm. Des."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1021\/ac403621c","article-title":"Monitoring of Human Chemical Signatures Using Membrane Inlet Mass Spectrometry","volume":"86","author":"Giannoukos","year":"2014","journal-title":"Anal. Chem."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s13361-014-1032-7","article-title":"Membrane Inlet Mass Spectrometry for Homeland Security and Forensic Applications","volume":"26","author":"Giannoukos","year":"2014","journal-title":"J. Am. Soc. Mass Spectrom."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.jchromb.2018.12.015","article-title":"Volatolomics: A Broad Area of Experimentation","volume":"1105","author":"Giannoukos","year":"2019","journal-title":"J. Chromatogr. B"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"027106","DOI":"10.1088\/1752-7163\/aa95dd","article-title":"Advances in Chemical Sensing Technologies for VOCs in Breath for Security\/Threat Assessment, Illicit Drug Detection, and Human Trafficking Activity","volume":"12","author":"Giannoukos","year":"2018","journal-title":"J. Breath Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"339","DOI":"10.3233\/BSI-120029","article-title":"A Comparison of Spectroscopic Techniques for Human Breath Analysis","volume":"1","author":"Chow","year":"2012","journal-title":"Biomed. Spectrosc. Imaging"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Piantadosi, S., and Meinert, C.L. (2022). ClinicalTrials.Gov. Principles and Practice of Clinical Trials, Springer International Publishing.","DOI":"10.1007\/978-3-319-52636-2"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1566","DOI":"10.1126\/science.1119426","article-title":"Ambient Mass Spectrometry","volume":"311","author":"Cooks","year":"2006","journal-title":"Science"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"034001","DOI":"10.1088\/1752-7155\/8\/3\/034001","article-title":"The Human Volatilome: Volatile Organic Compounds (VOCs) in Exhaled Breath, Skin Emanations, Urine, Feces and Saliva","volume":"8","author":"Amann","year":"2014","journal-title":"J. Breath Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6142","DOI":"10.1002\/smll.201501904","article-title":"Nanoscale Sensor Technologies for Disease Detection via Volatolomics","volume":"11","author":"Vishinkin","year":"2015","journal-title":"Small"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"11036","DOI":"10.1002\/anie.201500153","article-title":"Hybrid Volatolomics and Disease Detection","volume":"54","author":"Broza","year":"2015","journal-title":"Angew. Chem. Int. Ed."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1177\/154411130201300209","article-title":"The Diagnostic Applications of Saliva\u2014A Review","volume":"13","author":"Kaufman","year":"2002","journal-title":"Crit. Rev. Oral Biol. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1007\/s10886-010-9846-7","article-title":"Analysis of Volatile Organic Compounds in Human Saliva by a Static Sorptive Extraction Method and Gas Chromatography-Mass Spectrometry","volume":"36","author":"Soini","year":"2010","journal-title":"J. Chem. Ecol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.chroma.2019.05.016","article-title":"Development and Performance Evaluation of a Novel Dynamic Headspace Vacuum Transfer \u201cIn Trap\u201d Extraction Method for Volatile Compounds and Comparison with Headspace Solid-Phase Microextraction and Headspace in-Tube Extraction","volume":"1601","author":"Fuchsmann","year":"2019","journal-title":"J. Chromatogr. A"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.ijms.2004.08.010","article-title":"Applications of Breath Gas Analysis in Medicine","volume":"239","author":"Amann","year":"2004","journal-title":"Int. J. Mass Spectrom."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2327","DOI":"10.1002\/oby.21242","article-title":"Measuring Breath Acetone for Monitoring Fat Loss","volume":"23","author":"Anderson","year":"2015","journal-title":"Obesity"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8146","DOI":"10.1021\/acs.chemrev.6b00065","article-title":"Chemical Sniffing Instrumentation for Security Applications","volume":"116","author":"Giannoukos","year":"2016","journal-title":"Chem. Rev."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2135","DOI":"10.1021\/ac302752f","article-title":"Detection of Metabolites of Trapped Humans Using Ion Mobility Spectrometry Coupled with Gas Chromatography","volume":"85","author":"Vautz","year":"2013","journal-title":"Anal. Chem."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"029001","DOI":"10.1088\/1752-7155\/10\/2\/029001","article-title":"Breath Biomonitoring in National Security Assessment, Forensic THC Testing, Biomedical Technology and Quality Assurance Applications: Report from PittCon 2016","volume":"10","author":"Pleil","year":"2016","journal-title":"J. Breath Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1093\/anatox\/35.8.541","article-title":"Detection of \u03949-Tetrahydrocannabinol in Exhaled Breath Collected from Cannabis Users","volume":"35","author":"Beck","year":"2011","journal-title":"J. Anal. Toxicol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.trac.2014.11.018","article-title":"Trace Detection of Endogenous Human Volatile Organic Compounds for Search, Rescue and Emergency Applications","volume":"66","author":"Agapiou","year":"2015","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.ijms.2016.12.007","article-title":"Portable FAIMS: Applications and Future Perspectives","volume":"422","author":"Costanzo","year":"2017","journal-title":"Int. J. Mass Spectrom."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8230","DOI":"10.3390\/s91008230","article-title":"Breath Analysis Using Laser Spectroscopic Techniques: Breath Biomarkers, Spectral Fingerprints, and Detection Limits","volume":"9","author":"Wang","year":"2009","journal-title":"Sensors"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"014001","DOI":"10.1088\/1752-7155\/1\/1\/014001","article-title":"Recent Advances of Laser-Spectroscopy-Based Techniques for Applications in Breath Analysis","volume":"1","author":"McCurdy","year":"2007","journal-title":"J. Breath Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.jchromb.2018.01.013","article-title":"Monitoring of Selected Skin-and Breath-Borne Volatile Organic Compounds Emitted from the Human Body Using Gas Chromatography Ion Mobility Spectrometry (GC-IMS)","volume":"1076","author":"Mochalski","year":"2018","journal-title":"J. Chromatogr. B"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1016\/j.talanta.2017.10.025","article-title":"Comparison of Common Components Analysis with Principal Components Analysis and Independent Components Analysis: Application to SPME-GC-MS Volatolomic Signatures","volume":"178","author":"Bouhlel","year":"2018","journal-title":"Talanta"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.1007\/s10886-005-5801-4","article-title":"Comparison of the Volatile Organic Compounds Present in Human Odor Using SPME-GC\/MS","volume":"31","author":"Curran","year":"2005","journal-title":"J. Chem. Ecol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Phillips, M., Cataneo, R.N., Chaturvedi, A., Kaplan, P.D., Libardoni, M., Mundada, M., Patel, U., and Zhang, X. (2013). Detection of an Extended Human Volatome with Comprehensive Two-Dimensional Gas Chromatography Time-of-Flight Mass Spectrometry. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0075274"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"046006","DOI":"10.1088\/1752-7155\/5\/4\/046006","article-title":"The Trapped Human Experiment","volume":"5","author":"Huo","year":"2011","journal-title":"J. Breath Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/S0168-1176(97)00281-4","article-title":"On-Line Monitoring of Volatile Organic Compounds at Pptv Levels by Means of Proton-Transfer-Reaction Mass Spectrometry (PTR-MS) Medical Applications, Food Control and Environmental Research","volume":"173","author":"Lindinger","year":"1998","journal-title":"Int. J. Mass Spectrom. Ion Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"037110","DOI":"10.1088\/1752-7155\/7\/3\/037110","article-title":"Correlations between Blood Glucose and Breath Components from Portable Gas Sensors and PTR-TOF-MS","volume":"7","author":"Righettoni","year":"2013","journal-title":"J. Breath Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"L1","DOI":"10.1016\/0168-1176(95)04236-E","article-title":"Acetonitrile and Benzene in the Breath of Smokers and Non-Smokers Investigated by Proton Transfer Reaction Mass Spectrometry (PTR-MS)","volume":"148","author":"Jordan","year":"1995","journal-title":"Int. J. Mass Spectrom. Ion Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"026002","DOI":"10.1088\/1752-7155\/2\/2\/026002","article-title":"Compounds Enhanced in a Mass Spectrometric Profile of Smokers\u2019 Exhaled Breath versus Non-Smokers as Determined in a Pilot Study Using PTR-MS","volume":"2","author":"Kushch","year":"2008","journal-title":"J. Breath Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"L1","DOI":"10.1016\/S1387-3806(98)14153-2","article-title":"Quantification of Passive Smoking Using Proton-Transfer-Reaction Mass Spectrometry","volume":"178","author":"Prazeller","year":"1998","journal-title":"Int. J. Mass Spectrom."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1016\/j.trac.2011.05.001","article-title":"Direct, Rapid Quantitative Analyses of BVOCs Using SIFT-MS and PTR-MS Obviating Sample Collection","volume":"30","author":"Smith","year":"2011","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1088\/0967-3334\/27\/4\/001","article-title":"A Longitudinal Study of Ammonia, Acetone and Propanol in the Exhaled Breath of 30 Subjects Using Selected Ion Flow Tube Mass Spectrometry, SIFT-MS","volume":"27","author":"Turner","year":"2006","journal-title":"Physiol. Meas."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1088\/0967-3334\/27\/1\/002","article-title":"A Longitudinal Study of Breath Isoprene in Healthy Volunteers Using Selected Ion Flow Tube Mass Spectrometry (SIFT-MS)","volume":"27","author":"Turner","year":"2005","journal-title":"Physiol. Meas."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1088\/0967-3334\/27\/7\/007","article-title":"A Longitudinal Study of Methanol in the Exhaled Breath of 30 Healthy Volunteers Using Selected Ion Flow Tube Mass Spectrometry, SIFT-MS","volume":"27","author":"Turner","year":"2006","journal-title":"Physiol. Meas."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"6607","DOI":"10.1039\/C6AY00375C","article-title":"Analysis of Chlorinated Hydrocarbons in Gas Phase Using a Portable Membrane Inlet Mass Spectrometer","volume":"8","author":"Giannoukos","year":"2016","journal-title":"Anal. Methods"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1039\/C6AY03257E","article-title":"Portable Mass Spectrometry for the Direct Analysis and Quantification of Volatile Halogenated Hydrocarbons in the Gas Phase","volume":"9","author":"Giannoukos","year":"2017","journal-title":"Anal. Methods"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"11879","DOI":"10.1021\/acs.analchem.3c02669","article-title":"Ultrahigh Sensitivity PTR-MS Instrument with a Well-Defined Ion Chemistry","volume":"95","author":"Reinecke","year":"2023","journal-title":"Anal. Chem."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"17815","DOI":"10.1039\/D3CP02123H","article-title":"A SIFT-MS Study of Positive and Negative Ion Chemistry of the Ortho-, Meta-and Para-Isomers of Cymene, Cresol, and Ethylphenol","volume":"25","author":"Swift","year":"2023","journal-title":"Phys. Chem. Chem. Phys."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3507","DOI":"10.5194\/amt-13-3507-2020","article-title":"SIFT-MS Optimization for Atmospheric Trace Gas Measurements at Varying Humidity","volume":"13","author":"Lehnert","year":"2020","journal-title":"Atmospheric Meas. Tech."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4940","DOI":"10.1021\/acs.analchem.8b00237","article-title":"Sniffing Entrapped Humans with Sensor Arrays","volume":"90","author":"Pineau","year":"2018","journal-title":"Anal. Chem."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1007\/s13361-017-1660-9","article-title":"Comparison of Ambient and Atmospheric Pressure Ion Sources for Cystic Fibrosis Exhaled Breath Condensate Ion Mobility-Mass Spectrometry Metabolomics","volume":"28","author":"Zang","year":"2017","journal-title":"J. Am. Soc. Mass Spectrom."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Bregy, L., M\u00fcggler, A.R., Martinez-Lozano Sinues, P., Garc\u00eda-G\u00f3mez, D., Suter, Y., Belibasakis, G.N., Kohler, M., Schmidlin, P.R., and Zenobi, R. (2015). Differentiation of Oral Bacteria in in Vitro Cultures and Human Saliva by Secondary Electrospray Ionization\u2013Mass Spectrometry. Sci. Rep., 5.","DOI":"10.1038\/srep15163"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"6453","DOI":"10.1021\/acs.analchem.7b04600","article-title":"Real-Time Monitoring of Tricarboxylic Acid Metabolites in Exhaled Breath","volume":"90","author":"Singh","year":"2018","journal-title":"Anal. Chem."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"8526","DOI":"10.1039\/C6CC03070J","article-title":"Secondary Electrospray Ionization Coupled to High-Resolution Mass Spectrometry Reveals Tryptophan Pathway Metabolites in Exhaled Human Breath","volume":"52","author":"Gaisl","year":"2016","journal-title":"Chem. Commun."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Tsiara, A.A., Plakias, S., Kokkotis, C., Veneri, A., Mina, M.A., Tsiakiri, A., Kitmeridou, S., Christidi, F., Gourgoulis, E., and Doskas, T. (2025). Artificial Intelligence in the Diagnosis of Neurological Diseases Using Biomechanical and Gait Analysis Data: A Scopus-Based Bibliometric Analysis. Neurol. Int., 17.","DOI":"10.20944\/preprints202502.0105.v1"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1038\/s41586-021-04014-z","article-title":"Efficient and Targeted COVID-19 Border Testing via Reinforcement Learning","volume":"599","author":"Bastani","year":"2021","journal-title":"Nature"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1038\/d41586-021-02556-w","article-title":"A Machine-Learning Algorithm to Target COVID Testing of Travellers","volume":"599","author":"Obermeyer","year":"2021","journal-title":"Nature"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Kansizoglou, I., Kokkotis, C., Stampoulis, T., Giannakou, E., Siaperas, P., Kallidis, S., Koutra, M., Malliou, P., Michalopoulou, M., and Gasteratos, A. (2025). Artificial Intelligence and the Human\u2013Computer Interaction in Occupational Therapy: A Scoping Review. Algorithms, 18.","DOI":"10.3390\/a18050276"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Kokkotis, C., Kansizoglou, I., Stampoulis, T., Giannakou, E., Siaperas, P., Kallidis, S., Koutra, M., Koutra, C., Beneka, A., and Bebetsos, E. (2025). Artificial Intelligence as Assessment Tool in Occupational Therapy: A Scoping Review. BioMedInformatics, 5.","DOI":"10.3390\/biomedinformatics5020022"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s44230-023-00051-1","article-title":"Machine Learning Algorithms for the Prediction of Language and Cognition Rehabilitation Outcomes of Post-Stroke Patients: A Scoping Review","volume":"4","author":"Apostolidis","year":"2024","journal-title":"Hum. Centric Intell. Syst."},{"key":"ref_55","first-page":"3","article-title":"Updated Methodological Guidance for the Conduct of Scoping Reviews","volume":"19","author":"Peters","year":"2021","journal-title":"JBI Evid. Synth."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann. Intern. Med."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Gudi\u00f1o-Ochoa, A., Garc\u00eda-Rodr\u00edguez, J.A., Ochoa-Ornelas, R., Cuevas-Ch\u00e1vez, J.I., and S\u00e1nchez-Arias, D.A. (2024). Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose. Sensors, 24.","DOI":"10.3390\/s24041294"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"51712","DOI":"10.1109\/ACCESS.2023.3278278","article-title":"Automated Detection of Diabetes from Exhaled Human Breath Using Deep Hybrid Architecture","volume":"11","author":"Bhaskar","year":"2023","journal-title":"IEEE Access"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2309","DOI":"10.1021\/acssensors.3c00367","article-title":"Electronic Nose Development and Preliminary Human Breath Testing for Rapid, Non-Invasive COVID-19 Detection","volume":"8","author":"Li","year":"2023","journal-title":"ACS Sens."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Doguc, O., Silahtaroglu, G., Canbolat, Z.N., Hambarde, K., Gokay, H., and Ylmaz, M. (2023). Diagnosis of Covid-19 via Patient Breath Data Using Artificial Intelligence. arXiv.","DOI":"10.28991\/ESJ-2023-SPER-08"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Snitz, K., Andelman-Gur, M., Pinchover, L., Weissgross, R., Weissbrod, A., Mishor, E., Zoller, R., Linetsky, V., Medhanie, A., and Shushan, S. (2021). Proof of Concept for Real-Time Detection of SARS CoV-2 Infection with an Electronic Nose. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0252121"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"6671","DOI":"10.1007\/s00464-020-08169-0","article-title":"Applying the Electronic Nose for Pre-Operative SARS-CoV-2 Screening","volume":"35","author":"Wintjens","year":"2021","journal-title":"Surg. Endosc."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.cca.2021.10.005","article-title":"Detection of COPD and Lung Cancer with Electronic Nose Using Ensemble Learning Methods","volume":"523","author":"Binson","year":"2021","journal-title":"Clin. Chim. Acta"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"046003","DOI":"10.1088\/1752-7163\/ac1326","article-title":"Discrimination of COPD and Lung Cancer from Controls through Breath Analysis Using a Self-Developed e-Nose","volume":"15","author":"Binson","year":"2021","journal-title":"J. Breath Res."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1007\/s00542-024-05656-5","article-title":"Prediction of Lung Cancer with a Sensor Array Based E-Nose System Using Machine Learning Methods","volume":"30","author":"Binson","year":"2024","journal-title":"Microsyst. Technol."},{"key":"ref_66","first-page":"2509709","article-title":"A Weighted Discriminative Extreme Learning Machine Design for Lung Cancer Detection by an Electronic Nose System","volume":"70","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Lee, J.-M., Choi, E.J., Chung, J.H., Lee, K., Lee, Y., Kim, Y.-J., Kim, W.-G., Yoon, S.H., Seol, H.Y., and Devaraj, V. (2021). A DNA-Derived Phage Nose Using Machine Learning and Artificial Neural Processing for Diagnosing Lung Cancer. Biosens. Bioelectron., 194.","DOI":"10.1016\/j.bios.2021.113567"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"016004","DOI":"10.1088\/1752-7163\/ab433d","article-title":"Online Breath Analysis Using Metal Oxide Semiconductor Sensors (Electronic Nose) for Diagnosis of Lung Cancer","volume":"14","author":"Kononov","year":"2019","journal-title":"J. Breath Res."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Huang, C.-H., Zeng, C., Wang, Y.-C., Peng, H.-Y., Lin, C.-S., Chang, C.-J., and Yang, H.-Y. (2018). A Study of Diagnostic Accuracy Using a Chemical Sensor Array and a Machine Learning Technique to Detect Lung Cancer. Sensors, 18.","DOI":"10.3390\/s18092845"},{"key":"ref_70","first-page":"700","article-title":"Reducing the Electronic Nose Sensor Array for Asthma Detection Using Firefly Algorithm","volume":"17","author":"Rivai","year":"2024","journal-title":"Int. J. Intell. Eng. Syst."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1016\/j.jaci.2020.05.038","article-title":"eNose Breath Prints as a Surrogate Biomarker for Classifying Patients with Asthma by Atopy","volume":"146","author":"Brinkman","year":"2020","journal-title":"J. Allergy Clin. Immunol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"74924","DOI":"10.1109\/ACCESS.2023.3291451","article-title":"Optimization of the Electronic Nose Sensor Array for Asthma Detection Based on Genetic Algorithm","volume":"11","author":"Aulia","year":"2023","journal-title":"IEEE Access"},{"key":"ref_73","first-page":"264","article-title":"Identification of Chronic Obstructive Pulmonary Disease Using Graph Convolutional Network in Electronic Nose","volume":"34","author":"Aulia","year":"2024","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"4934","DOI":"10.1021\/acssensors.4c01584","article-title":"Olfactory Diagnosis Model for Lung Health Evaluation Based on Pyramid Pooling and SHAP-Based Dual Encoders","volume":"9","author":"Peng","year":"2024","journal-title":"ACS Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"25163","DOI":"10.1109\/ACCESS.2025.3536308","article-title":"A New Diagnosing Method for Psoriasis from Exhaled Breath","volume":"13","author":"Tozlu","year":"2025","journal-title":"IEEE Access"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1021\/acssensors.4c02073","article-title":"TC-Sniffer: A Transformer-CNN Bibranch Framework Leveraging Auxiliary VOCs for Few-Shot UBC Diagnosis via Electronic Noses","volume":"10","author":"Jian","year":"2024","journal-title":"ACS Sens."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"G\u00f3mez, J.K.C., V\u00e1squez, C.A.C., Acevedo, C.M.D., and Llecha, J.B. (2024). Assessing Data Fusion in Sensory Devices for Enhanced Prostate Cancer Detection Accuracy. Chemosensors, 12.","DOI":"10.3390\/chemosensors12110228"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"2835","DOI":"10.32604\/iasc.2023.034857","article-title":"The IOMT-Based Risk-Free Approach to Lung Disorders Detection from Exhaled Breath Examination","volume":"36","author":"Ghani","year":"2023","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1097\/LGT.0000000000000707","article-title":"Development of an Algorithm for Cervical High-Grade Squamous Intraepithelial Lesion Based on Breath Print Analysis","volume":"27","author":"Dokter","year":"2023","journal-title":"J. Low. Genit. Tract Dis."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Ketchanji Mougang, Y.C., Endale Mangamba, L.-M., Capuano, R., Ciccacci, F., Catini, A., Paolesse, R., Mbatchou Ngahane, H.B., Palombi, L., and Di Natale, C. (2023). On-Field Test of Tuberculosis Diagnosis through Exhaled Breath Analysis with a Gas Sensor Array. Biosensors, 13.","DOI":"10.3390\/bios13050570"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"036001","DOI":"10.1088\/1752-7163\/ac5f13","article-title":"Engineering Solutions to Breath Tests Based on an E-Nose System for Silicosis Screening and Early Detection in Miners","volume":"16","author":"Xuan","year":"2022","journal-title":"J. Breath Res."},{"key":"ref_82","first-page":"394","article-title":"A New Approach for Detection of Viral Respiratory Infections Using E-Nose Through Sweat from Armpit with Fully Connected Deep Network","volume":"15","author":"Malikhah","year":"2022","journal-title":"Int. J. Intell. Eng. Syst."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"20886","DOI":"10.1109\/JSEN.2021.3100390","article-title":"Prediction of Pulmonary Diseases with Electronic Nose Using SVM and XGBoost","volume":"21","author":"Binson","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"115650","DOI":"10.1016\/j.sna.2024.115650","article-title":"A Single Gas Sensor Assisted by Machine Learning Algorithms for Breath-Based Detection of COPD: A Pilot Study","volume":"376","author":"Mahdavi","year":"2024","journal-title":"Sens. Actuators Phys."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.tcs.2022.08.021","article-title":"Chronic Obstructive Pulmonary Disease Prediction Using Internet of Things-Spiro System and Fuzzy-Based Quantum Neural Network Classifier","volume":"941","author":"Karthick","year":"2023","journal-title":"Theor. Comput. Sci."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Avian, C., Mahali, M.I., Putro, N.A.S., Prakosa, S.W., and Leu, J.-S. (2022). Fx-Net and PureNet: Convolutional Neural Network Architecture for Discrimination of Chronic Obstructive Pulmonary Disease from Smokers and Healthy Subjects through Electronic Nose Signals. Comput. Biol. Med., 148.","DOI":"10.1016\/j.compbiomed.2022.105913"},{"key":"ref_87","first-page":"4077","article-title":"An Interpretable Deep Learning Based Approach for Chronic Obstructive Pulmonary Disease Using Explainable Artificial Intelligence","volume":"17","author":"Dahy","year":"2024","journal-title":"Int. J. Inf. Technol."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"107945","DOI":"10.1016\/j.compeleceng.2022.107945","article-title":"A Machine Learning Approach for Human Breath Diagnosis with Soft Sensors","volume":"100","author":"Suresh","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Po\u013caka, I., Me\u017emale, L., Anarkulova, L., Kononova, E., Vilkoite, I., Veliks, V., \u013be\u0161\u010dinska, A.M., Ston\u0101ns, I., P\u010dolkins, A., and Tolmanis, I. (2023). The Detection of Colorectal Cancer through Machine Learning-Based Breath Sensor Analysis. Diagnostics, 13.","DOI":"10.3390\/diagnostics13213355"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Polaka, I., Bhandari, M.P., Mezmale, L., Anarkulova, L., Veliks, V., Sivins, A., Lescinska, A.M., Tolmanis, I., Vilkoite, I., and Ivanovs, I. (2022). Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection. Diagnostics, 12.","DOI":"10.3390\/diagnostics12020491"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-S\u00e1nchez, C., Barbarroja, N., Pantale\u00e3o, L.C., L\u00f3pez-S\u00e1nchez, L.M., Ozanne, S.E., Jurado-G\u00e1mez, B., Aranda, E., Lopez-Pedrera, C., and Rodr\u00edguez-Ariza, A. (2021). Clinical Utility of microRNAs in Exhaled Breath Condensate as Biomarkers for Lung Cancer. J. Pers. Med., 11.","DOI":"10.3390\/jpm11020111"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"135578","DOI":"10.1016\/j.snb.2024.135578","article-title":"Breath Analysis System with Convolutional Neural Network (CNN) for Early Detection of Lung Cancer","volume":"409","author":"Lee","year":"2024","journal-title":"Sens. Actuators B Chem."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"102063","DOI":"10.1016\/j.rineng.2024.102063","article-title":"Analyzing the Performance of a Bio-Sensor Integrated Improved Blended Learning Model for Accurate Pneumonia Prediction","volume":"22","author":"Lekshmy","year":"2024","journal-title":"Results Eng."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/s41746-022-00661-2","article-title":"Fast and Noninvasive Electronic Nose for Sniffing out COVID-19 Based on Exhaled Breath-Print Recognition","volume":"5","author":"Nurputra","year":"2022","journal-title":"NPJ Digit. Med."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"102323","DOI":"10.1016\/j.artmed.2022.102323","article-title":"Hybrid Learning Method Based on Feature Clustering and Scoring for Enhanced COVID-19 Breath Analysis by an Electronic Nose","volume":"129","author":"Hidayat","year":"2022","journal-title":"Artif. Intell. Med."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"12125","DOI":"10.1021\/acsnano.0c05657","article-title":"Multiplexed Nanomaterial-Based Sensor Array for Detection of COVID-19 in Exhaled Breath","volume":"14","author":"Shan","year":"2020","journal-title":"ACS Nano"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"047104","DOI":"10.1088\/1752-7163\/acf1bf","article-title":"Evaluation of Different Classification Methods Using Electronic Nose Data to Diagnose Sarcoidosis","volume":"17","author":"Spiekerman","year":"2023","journal-title":"J. Breath Res."},{"key":"ref_98","first-page":"144","article-title":"CNN-CatBoost Ensemble Deep Learning Model for Enhanced Disease Detection and Classification of Kidney Disease","volume":"34","author":"Bhaskar","year":"2024","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.11591\/ijece.v14i2.pp2035-2042","article-title":"An Efficient Convolutional Neural Network-Extreme Gradient Boosting Hybrid Deep Learning Model for Disease Detection Applications","volume":"14","author":"Bhaskar","year":"2024","journal-title":"Int. J. Electr. Comput. Eng. IJECE"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"1630","DOI":"10.1109\/JBHI.2017.2757510","article-title":"Real-Time Non-Invasive Detection and Classification of Diabetes Using Modified Convolution Neural Network","volume":"22","author":"Lekha","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"154256","DOI":"10.1016\/j.jcrc.2023.154256","article-title":"Reverse Triggering Neural Network and Rules-Based Automated Detection in Acute Respiratory Distress Syndrome","volume":"75","author":"Glowala","year":"2023","journal-title":"J. Crit. Care"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.healun.2022.09.009","article-title":"Diagnostic Performance of Electronic Nose Technology in Chronic Lung Allograft Dysfunction","volume":"42","author":"Wijbenga","year":"2023","journal-title":"J. Heart Lung Transplant."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"016003","DOI":"10.1088\/1752-7163\/ad7978","article-title":"Discovery and Analysis of the Relationship between Organic Components in Exhaled Breath and Bronchiectasis","volume":"19","author":"Fan","year":"2024","journal-title":"J. Breath Res."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"00206","DOI":"10.1183\/23120541.00206-2023","article-title":"Enhanced Real-Time Mass Spectrometry Breath Analysis for the Diagnosis of COVID-19","volume":"9","author":"Roquencourt","year":"2023","journal-title":"ERJ Open Res."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"101207","DOI":"10.1016\/j.eclinm.2021.101207","article-title":"A Method for the Identification of COVID-19 Biomarkers in Human Breath Using Proton Transfer Reaction Time-of-Flight Mass Spectrometry","volume":"42","author":"Liangou","year":"2021","journal-title":"EClinicalMedicine"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Grassin-Delyle, S., Roquencourt, C., Moine, P., Saffroy, G., Carn, S., Heming, N., Fleuriet, J., Salvator, H., Naline, E., and Couderc, L.-J. (2021). Metabolomics of Exhaled Breath in Critically Ill COVID-19 Patients: A Pilot Study. EBioMedicine, 63.","DOI":"10.1016\/j.ebiom.2020.103154"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Weber, R., Streckenbach, B., Welti, L., Inci, D., Kohler, M., Perkins, N., Zenobi, R., Micic, S., and Moeller, A. (2023). Online Breath Analysis with SESI\/HRMS for Metabolic Signatures in Children with Allergic Asthma. Front. Mol. Biosci., 10.","DOI":"10.3389\/fmolb.2023.1154536"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Fu, L., Wang, L., Wang, H., Yang, M., Yang, Q., Lin, Y., Guan, S., Deng, Y., Liu, L., and Li, Q. (2023). A Cross-Sectional Study: A Breathomics Based Pulmonary Tuberculosis Detection Method. BMC Infect. Dis., 23.","DOI":"10.1186\/s12879-023-08112-3"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Rai, S.N., Das, S., Pan, J., Mishra, D.C., and Fu, X.-A. (2022). Multigroup Prediction in Lung Cancer Patients and Comparative Controls Using Signature of Volatile Organic Compounds in Breath Samples. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0277431"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Tsou, P.-H., Lin, Z.-L., Pan, Y.-C., Yang, H.-C., Chang, C.-J., Liang, S.-K., Wen, Y.-F., Chang, C.-H., Chang, L.-Y., and Yu, K.-L. (2021). Exploring Volatile Organic Compounds in Breath for High-Accuracy Prediction of Lung Cancer. Cancers, 13.","DOI":"10.3390\/cancers13061431"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Butcher, J.B., Rutter, A.V., Wootton, A.J., Day, C.R., and Sul\u00e9-Suso, J. (2017). Artificial Neural Network Analysis of Volatile Organic Compounds for the Detection of Lung Cancer, Springer.","DOI":"10.1007\/978-3-319-66939-7_15"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Chen, D., Bryden, W.A., and Wood, R. (2020). Detection of Tuberculosis by the Analysis of Exhaled Breath Particles with High-Resolution Mass Spectrometry. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-64637-6"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"342883","DOI":"10.1016\/j.aca.2024.342883","article-title":"Identification Potential Biomarkers for Diagnosis, and Progress of Breast Cancer by Using High-Pressure Photon Ionization Time-of-Flight Mass Spectrometry","volume":"1320","author":"Zhang","year":"2024","journal-title":"Anal. Chim. Acta"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"119733","DOI":"10.1016\/j.cca.2024.119733","article-title":"Exhaled Breath Analysis in Adult Patients with Cystic Fibrosis by Real-Time Proton Mass Spectrometry","volume":"560","author":"Mustafina","year":"2024","journal-title":"Clin. Chim. Acta"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"4852","DOI":"10.1002\/alz.13053","article-title":"A Detection Model for Cognitive Dysfunction Based on Volatile Organic Compounds from a Large Chinese Community Cohort","volume":"19","author":"Jiao","year":"2023","journal-title":"Alzheimers Dement."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1002\/hep4.1499","article-title":"Breath Metabolomics Provides an Accurate and Noninvasive Approach for Screening Cirrhosis, Primary, and Secondary Liver Tumors","volume":"4","author":"Grove","year":"2020","journal-title":"Hepatol. Commun."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"E117","DOI":"10.1503\/jpn.220139","article-title":"Gut\u2013Brain Axis Volatile Organic Compounds Derived from Breath Distinguish between Schizophrenia and Major Depressive Disorder","volume":"48","author":"Henning","year":"2023","journal-title":"J. Psychiatry Neurosci."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"107534","DOI":"10.1016\/j.rmed.2024.107534","article-title":"Disease Diagnosis and Severity Classification in Pulmonary Fibrosis Using Carbonyl Volatile Organic Compounds in Exhaled Breath","volume":"222","author":"Taylor","year":"2024","journal-title":"Respir. Med."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"036004","DOI":"10.1088\/1752-7163\/accfb8","article-title":"Volatolomics Analysis of Exhaled Breath and Gastric-Endoluminal Gas for Distinguishing Early Upper Gastrointestinal Cancer from Benign","volume":"17","author":"Xiang","year":"2023","journal-title":"J. Breath Res."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s12014-023-09403-2","article-title":"Proteomic Characteristics and Diagnostic Potential of Exhaled Breath Particles in Patients with COVID-19","volume":"20","author":"Hirdman","year":"2023","journal-title":"Clin. Proteom."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"016005","DOI":"10.1088\/1752-7163\/aca119","article-title":"Intelligent COVID-19 Screening Platform Based on Breath Analysis","volume":"17","author":"Xue","year":"2022","journal-title":"J. Breath Res."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"036002","DOI":"10.1088\/1752-7163\/ac696a","article-title":"Exhaled VOCs Can Discriminate Subjects with COVID-19 from Healthy Controls","volume":"16","author":"Woollam","year":"2022","journal-title":"J. Breath Res."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"046009","DOI":"10.1088\/1752-7163\/ac8ea1","article-title":"A Feasibility Study of Covid-19 Detection Using Breath Analysis by High-Pressure Photon Ionization Time-of-Flight Mass Spectrometry","volume":"16","author":"Zhang","year":"2022","journal-title":"J. Breath Res."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"340752","DOI":"10.1016\/j.aca.2022.340752","article-title":"Identification of Phenol 2, 2-Methylene Bis, 6 [1, 1-D] as Breath Biomarker of Hepatocellular Carcinoma (HCC) Patients and Its Electrochemical Sensing: E-Nose Biosensor for HCC","volume":"1242","author":"Nazir","year":"2023","journal-title":"Anal. Chim. Acta"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"42613","DOI":"10.1021\/acsomega.2c06132","article-title":"Non-Invasive Exhaled Breath and Skin Analysis to Diagnose Lung Cancer: Study of Age Effect on Diagnostic Accuracy","volume":"7","author":"Gashimova","year":"2022","journal-title":"ACS Omega"},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Koureas, M., Kalompatsios, D., Amoutzias, G.D., Hadjichristodoulou, C., Gourgoulianis, K., and Tsakalof, A. (2021). Comparison of Targeted and Untargeted Approaches in Breath Analysis for the Discrimination of Lung Cancer from Benign Pulmonary Diseases and Healthy Persons. Molecules, 26.","DOI":"10.3390\/molecules26092609"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Koureas, M., Kirgou, P., Amoutzias, G., Hadjichristodoulou, C., Gourgoulianis, K., and Tsakalof, A. (2020). Target Analysis of Volatile Organic Compounds in Exhaled Breath for Lung Cancer Discrimination from Other Pulmonary Diseases and Healthy Persons. Metabolites, 10.","DOI":"10.3390\/metabo10080317"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"112067","DOI":"10.1016\/j.microc.2024.112067","article-title":"Identification of Volatile Biomarkers in Exhaled Breath by Polythiophene Solid Phase Microextraction Fiber for Disease Diagnosis Using GC\u2013MS","volume":"207","author":"Pelit","year":"2024","journal-title":"Microchem. J."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"e00518","DOI":"10.14309\/ctg.0000000000000518","article-title":"Detecting Colorectal Adenomas and Cancer Using Volatile Organic Compounds in Exhaled Breath: A Proof-of-Principle Study to Improve Screening","volume":"13","author":"Cheng","year":"2022","journal-title":"Clin. Transl. Gastroenterol."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1007\/s12553-023-00787-7","article-title":"Selection of Consistent Breath Biomarkers of Abnormal Liver Function Using Feature Selection: A Pilot Study","volume":"13","author":"Patnaik","year":"2023","journal-title":"Health Technol."},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Patnaik, R.K., Lin, Y.-C., Agarwal, A., Ho, M.-C., and Yeh, J.A. (2022). A Pilot Study for the Prediction of Liver Function Related Scores Using Breath Biomarkers and Machine Learning. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-05808-5"},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Khan, M.S., Cuda, S., Karere, G.M., Cox, L.A., and Bishop, A.C. (2022). Breath Biomarkers of Insulin Resistance in Pre-Diabetic Hispanic Adolescents with Obesity. Sci. Rep., 12.","DOI":"10.1038\/s41598-021-04072-3"},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Di Gilio, A., Catino, A., Lombardi, A., Palmisani, J., Facchini, L., Mongelli, T., Varesano, N., Bellotti, R., Galetta, D., and de Gennaro, G. (2020). Breath Analysis for Early Detection of Malignant Pleural Mesothelioma: Volatile Organic Compounds (VOCs) Determination and Possible Biochemical Pathways. Cancers, 12.","DOI":"10.3390\/cancers12051262"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"016005","DOI":"10.1088\/1752-7163\/aae80e","article-title":"Exhaled Human Breath Analysis in Active Pulmonary Tuberculosis Diagnostics by Comprehensive Gas Chromatography-Mass Spectrometry and Chemometric Techniques","volume":"13","author":"Beccaria","year":"2018","journal-title":"J. Breath Res."},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Bobak, C.A., Kang, L., Workman, L., Bateman, L., Khan, M.S., Prins, M., May, L., Franchina, F.A., Baard, C., and Nicol, M.P. (2021). Breath Can Discriminate Tuberculosis from Other Lower Respiratory Illness in Children. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-80970-w"},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Sharma, R., Zang, W., Zhou, M., Schafer, N., Begley, L.A., Huang, Y.J., and Fan, X. (2021). Real Time Breath Analysis Using Portable Gas Chromatography for Adult Asthma Phenotypes. Metabolites, 11.","DOI":"10.3390\/metabo11050265"},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Wieczorek, M., Weston, A., Ledenko, M., Thomas, J.N., Carter, R., and Patel, T. (2022). A Deep Learning Approach for Detecting Liver Cirrhosis from Volatolomic Analysis of Exhaled Breath. Front. Med., 9.","DOI":"10.3389\/fmed.2022.992703"},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Thomas, J.N., Roopkumar, J., and Patel, T. (2021). Machine Learning Analysis of Volatolomic Profiles in Breath Can Identify Non-Invasive Biomarkers of Liver Disease: A Pilot Study. PLoS ONE, 16.","DOI":"10.21203\/rs.3.rs-192281\/v1"},{"key":"ref_139","first-page":"047104","article-title":"COVID-19 Screening Using Breath-Borne Volatile Organic Compounds","volume":"15","author":"Chen","year":"2021","journal-title":"J. Breath Res."},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Mentel, S., Gallo, K., Wagendorf, O., Preissner, R., Nahles, S., Heiland, M., and Preissner, S. (2021). Prediction of Oral Squamous Cell Carcinoma Based on Machine Learning of Breath Samples: A Prospective Controlled Study. BMC Oral Health, 21.","DOI":"10.1186\/s12903-021-01862-z"},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Sukaram, T., Apiparakoon, T., Tiyarattanachai, T., Ariyaskul, D., Kulkraisri, K., Marukatat, S., Rerknimitr, R., and Chaiteerakij, R. (2023). VOCs from Exhaled Breath for the Diagnosis of Hepatocellular Carcinoma. Diagnostics, 13.","DOI":"10.3390\/diagnostics13020257"},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Golyak, I.S., Anfimov, D.R., Demkin, P.P., Berezhanskiy, P.V., Nebritova, O.A., Morozov, A.N., and Fufurin, I.L. (2024). A Hybrid Learning Approach to Better Classify Exhaled Breath\u2019s Infrared Spectra: A Noninvasive Optical Diagnosis for Socially Significant Diseases. J. Biophotonics, 17.","DOI":"10.1002\/jbio.202400151"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"036001","DOI":"10.1088\/1752-7163\/acc6e4","article-title":"Breath Analysis by Ultra-Sensitive Broadband Laser Spectroscopy Detects SARS-CoV-2 Infection","volume":"17","author":"Liang","year":"2023","journal-title":"J. Breath Res."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"101308","DOI":"10.1016\/j.eclinm.2022.101308","article-title":"Detection of SARS-CoV-2 Infection by Exhaled Breath Spectral Analysis: Introducing a Ready-to-Use Point-of-Care Mass Screening Method","volume":"45","author":"Shlomo","year":"2022","journal-title":"EClinicalMedicine"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"026009","DOI":"10.1088\/1752-7163\/ad2b6e","article-title":"Machine Learning Enabled Detection of COVID-19 Pneumonia Using Exhaled Breath Analysis: A Proof-of-Concept Study","volume":"18","author":"Cusack","year":"2024","journal-title":"J. Breath Res."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"027104","DOI":"10.1088\/1752-7163\/abebd4","article-title":"Application of Machine Learning and Laser Optical-Acoustic Spectroscopy to Study the Profile of Exhaled Air Volatile Markers of Acute Myocardial Infarction","volume":"15","author":"Borisov","year":"2021","journal-title":"J. Breath Res."},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Kistenev, Y.V., Borisov, A.V., Nikolaev, V.V., Vrazhnov, D.A., and Kuzmin, D.A. (2019). Laser Photoacoustic Spectroscopy Applications in Breathomics. J. Biomed. Photonics Eng., 5.","DOI":"10.18287\/JBPE19.05.010303"},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Xie, X., Yu, W., Wang, L., Yang, J., Tu, X., Liu, X., Liu, S., Zhou, H., Chi, R., and Huang, Y. (2024). SERS-Based AI Diagnosis of Lung and Gastric Cancer via Exhaled Breath. Spectrochim. Acta Part A Mol. Biomol. Spectrosc., 314.","DOI":"10.1016\/j.saa.2024.124181"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"3487","DOI":"10.1021\/acssensors.3c01061","article-title":"Construction of High-Active SERS Cavities in a TiO2 Nanochannels-Based Membrane: A Selective Device for Identifying Volatile Aldehyde Biomarkers","volume":"8","author":"Xu","year":"2023","journal-title":"ACS Sens."},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Aslam, M.A., Xue, C., Chen, Y., Zhang, A., Liu, M., Wang, K., and Cui, D. (2021). Breath Analysis Based Early Gastric Cancer Classification from Deep Stacked Sparse Autoencoder Neural Network. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-83184-2"},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1021\/acssensors.2c02001","article-title":"Multiarray Biosensor for Diagnosing Lung Cancer Based on Gap Plasmonic Color Films","volume":"8","author":"Nguyen","year":"2022","journal-title":"ACS Sens."},{"key":"ref_152","doi-asserted-by":"crossref","unstructured":"Picciariello, A., Dezi, A., Vincenti, L., Spampinato, M.G., Zang, W., Riahi, P., Scott, J., Sharma, R., Fan, X., and Altomare, D.F. (2024). Colorectal Cancer Diagnosis through Breath Test Using a Portable Breath Analyzer\u2014Preliminary Data. Sensors, 24.","DOI":"10.3390\/s24072343"},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"6435","DOI":"10.1007\/s00216-019-02024-5","article-title":"Rapid Breath Analysis for Acute Respiratory Distress Syndrome Diagnostics Using a Portable Two-Dimensional Gas Chromatography Device","volume":"411","author":"Zhou","year":"2019","journal-title":"Anal. Bioanal. Chem."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/11\/968\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T05:30:58Z","timestamp":1762925458000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/11\/968"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":153,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["info16110968"],"URL":"https:\/\/doi.org\/10.3390\/info16110968","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,10]]}}}