Evaluating the VOCORDER device for early disease detection through breath analysis: study protocol for a two-phase...

Area of Science:

• Biomedical Engineering
• Analytical Chemistry
• Clinical Diagnostics

Background:

• Current disease diagnosis relies on late-stage symptom detection and invasive methods.
• Exhaled breath analysis offers a non-invasive alternative for early disease identification via volatile organic compounds (VOCs).
• The VOCORDER project aims to create a cost-effective device for continuous health monitoring through breath analysis.

Purpose of the Study:

• To develop a novel device for early disease detection using exhaled breath analysis.
• To identify and validate volatile organic compound (VOC) profiles associated with specific life-threatening diseases.
• To establish a fast, user-friendly, and cost-effective method for continuous health monitoring.

Main Methods:

• Literature review to identify target diseases and VOC profiles.
• Clinical trial with two phases: baseline (biomarker discovery) and validation.
• Utilizing gas chromatography-mass spectrometry (GC-MS) and SESI-HR-MS for breath sample analysis.
• Development and testing of artificial intelligence (AI) models for disease prediction.
• Evaluation of the VOCORDER device performance with AI models.

Main Results:

• Identification of potential breath biomarkers and VOC profiles for early disease diagnosis.
• Development of AI models for analyzing VOC data.
• Validation of the VOCORDER device's efficacy in detecting diseases through breath analysis.

Conclusions:

• Breath analysis holds significant potential for non-invasive, early disease detection.
• The VOCORDER device and associated AI models offer a promising approach for continuous health monitoring.
• This technology could revolutionize disease diagnosis by enabling detection before symptom onset.