Clinical evaluation of the electronic nose in the diagnosis of ear, nose and throat infection: a preliminary study

2004 ◽  
Vol 118 (09) ◽  
Author(s):  
M.E. Shykhon ◽  
D.W. Morgan ◽  
R. Dutta ◽  
E.L. Hines ◽  
J.W. Gardner
Keyword(s):  
Clinical Evaluation ◽  
Electronic Nose ◽  
Throat Infection ◽  
Preliminary Study ◽  
Nose And Throat

Clinical Evaluation of a Direct Method for the Intrusion of Supraerupted Maxillary Molars: A Preliminary Study

2015 ◽  
Vol 28 (6) ◽  
pp. 610-611 ◽  
Author(s):  
Célia Pinzan-Vercelino ◽  
Júlio Gurgel ◽  
Kellyne Carvalho ◽  
Carollyne Tiago ◽  
Arnaldo Pinzan
Keyword(s):  
Clinical Evaluation ◽  
Direct Method ◽  
Maxillary Molars ◽  
Preliminary Study ◽  
A Direct Method

Attitude of parents in a Niger Delta community towards ear, nose and throat diseases among their children: A preliminary study

2019 ◽  
Vol 13 (3) ◽  
pp. 98
Author(s):  
Bright Otoghile ◽  
MatildaUju Ibekwe ◽  
EbidisemeneLinda Totyen ◽  
AppahNaomi Inei
Keyword(s):  
Niger Delta ◽  
Preliminary Study ◽  
Nose And Throat

Classification of Ear, Nose, and Throat Bacteria Using a Neural-Network-Based Electronic Nose

MRS Bulletin ◽  
2004 ◽  
Vol 29 (10) ◽  
pp. 709-713 ◽  
Author(s):  
Ritaban Dutta ◽  
Julian W. Gardner ◽  
Evor L. Hines
Keyword(s):  
Feature Extraction ◽  
Electronic Nose ◽  
Clustering Algorithms ◽  
Complex Mixture ◽  
Complex Data ◽  
Electronic Noses ◽  
Combined Use ◽  
Precise Diagnosis ◽  
Nose And Throat

AbstractThis article describes the use of an electronic nose (the Cyranose 320) to sense and identify three species of bacteria responsible for ear, nose, and throat (ENT) infections. Gathered data were a complex mixture of different chemical compounds. An innovative approach for classifying the bacteria data was investigated by using a combination of several clustering algorithms. The best results suggest that the three classes of bacteria examined can be predicted with up to 98% accuracy, allowing more precise diagnosis of ENT infection in patients. This type of bacteria data analysis and feature extraction is difficult, but it can be concluded that combined use of the analysis methods described here can solve the feature extraction problem with very complex data and enhance the performance of electronic noses.

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