TY - JOUR
T1 - A Novel Method for Diagnosing Chronic Rhinosinusitis based on an Electronic Nose
AU - Bruno, Ernesto
AU - Linder, Roland
AU - Alessandrini, Marco
AU - Pöppl, Siegfried J.
AU - Puija, Alberto
AU - Mohamed, E. I.
AU - De Girolamo, L.
AU - De Lorenzo, A.
PY - 2003/11/1
Y1 - 2003/11/1
N2 - The nasal out-breath of persons with chronic nasal and/or paranasal infections may have characteristic strange odors, which in our experience are in most cases related to bacterial and/or fungal infections of the sinuses. The objective of the present study was to examine nasal out-breath samples from patients with chronic rhinosinusitis (CRS) (with or without polyposis) and healthy control volunteers using the electronic-nose (EN) technology. We developed a simple technique for collecting samples of nasal out-breath in disposable sterile plastic sacks with a tight closing seal. The principal component analysis correctly classified all individual EN patterns for CRS patients and misclassified 2 samples from the healthy controls (80.0% successful classification rate). The artificial neural network analysis correctly classified 60.0% of the patterns of both groups. We believe that the use of methodologies based on EN technology, combined with conventional clinical examinations, may improve the diagnosis of chronic rhinosinusitis.
AB - The nasal out-breath of persons with chronic nasal and/or paranasal infections may have characteristic strange odors, which in our experience are in most cases related to bacterial and/or fungal infections of the sinuses. The objective of the present study was to examine nasal out-breath samples from patients with chronic rhinosinusitis (CRS) (with or without polyposis) and healthy control volunteers using the electronic-nose (EN) technology. We developed a simple technique for collecting samples of nasal out-breath in disposable sterile plastic sacks with a tight closing seal. The principal component analysis correctly classified all individual EN patterns for CRS patients and misclassified 2 samples from the healthy controls (80.0% successful classification rate). The artificial neural network analysis correctly classified 60.0% of the patterns of both groups. We believe that the use of methodologies based on EN technology, combined with conventional clinical examinations, may improve the diagnosis of chronic rhinosinusitis.
UR - https://www.researchgate.net/publication/8982102_A_novel_method_for_diagnosing_chronic_rhinosinusitis_based_on_an_electronic_nose
UR - https://www.academia.edu/2772617/A_novel_method_for_diagnosing_chronic_rhinosinusitis_based_on_an_electronic_nose
UR - https://www.ncbi.nlm.nih.gov/pubmed/14648925?dopt=Abstract
M3 - Journal articles
SN - 0303-8874
VL - 30
SP - 447
EP - 457
JO - Anales otorrinolaringológicos ibero-americanos
JF - Anales otorrinolaringológicos ibero-americanos
ER -
