A novel method for diagnosing chronic rhinosinusitis based on an electronic nose

Area of Science:

• Otolaryngology
• Biomedical Engineering
• Analytical Chemistry

Background:

• Chronic rhinosinusitis (CRS) is often associated with distinct nasal odors.
• These odors are frequently linked to underlying bacterial or fungal sinus infections.
• Accurate diagnosis of CRS can be challenging.

Purpose of the Study:

• To investigate the utility of electronic-nose (EN) technology for analyzing nasal out-breath in CRS patients.
• To differentiate between nasal breath patterns of CRS patients and healthy controls.
• To assess the potential of EN technology in improving CRS diagnosis.

Main Methods:

• Development of a simple method for collecting nasal out-breath samples in sterile plastic sacks.
• Utilizing electronic-nose (EN) technology to analyze volatile organic compounds in breath samples.
• Application of principal component analysis (PCA) and artificial neural network (ANN) analysis for pattern recognition.

Main Results:

• Principal component analysis achieved an 80.0% successful classification rate between CRS patients and healthy controls.
• Artificial neural network analysis correctly classified 60.0% of patterns for both groups.
• Distinct EN patterns were observed for CRS patients compared to healthy individuals.

Conclusions:

• Electronic-nose (EN) technology can identify characteristic patterns in the nasal out-breath of CRS patients.
• EN analysis of nasal breath shows potential as a non-invasive diagnostic tool for chronic rhinosinusitis.
• Combining EN technology with clinical examinations may enhance the diagnostic accuracy for CRS.