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Exhaled Breath Electronic Nose for Multi-Disease Risk Prescreening

The First Affiliated Hospital of Guangzhou Medical University · 2026
ClinicalTrials.gov · Datasets · License: Public Domain · 2026
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artificial-intelligencelung-cancermachine-learningmetabolic-disease
This prospective multicenter observational study will evaluate whether an electronic nose that analyzes patterns in exhaled breath can be developed as a noninvasive tool for early risk prescreening across several disease areas. The study will enroll up to 20,000 adults from hospitals and health examination centers. It will first establish a standardized breath-sampling and quality-control procedure and assess repeatability across breathing maneuvers, devices, centers, and time. It will then develop and independently evaluate risk prescreening models for prespecified disease modules, including lung cancer/clinically significant pulmonary nodules, chronic respiratory diseases, and metabolic/endocrine diseases, with additional exploratory modules involving liver, kidney, cardiovascular, and systemic inflammatory/infectious/immune conditions. The electronic-nose results are for research only and will not be reported to participants or clinicians or used to change routine screening, diagnosis, or treatment. Disease reference status will be determined from examinations, laboratory tests, imaging, pathology, pulmonary function testing, specialist assessments, or follow-up that are already performed as part of routine care. The study will evaluate screening sensitivity, specificity, false-negative rate, predictive values, calibration, and performance across different centers, devices, and time periods.
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