Classification of root canal microorganisms using electronic-nose and discriminant analysis.

BACKGROUND: Root canal treatment is a debridement process which disrupts and removes entire microorganisms from the root canal system. Identification of microorganisms may help clinicians decide on treatment alternatives such as using different irrigants, intracanal medicaments and antibiotics. However, the difficulty in cultivation and the complexity in isolation of predominant anaerobic microorganisms make clinicians resort to empirical medical treatments. For this reason, identification of microorganisms is not a routinely used procedure in root canal treatment. In this study, we aimed at classifying 7 different standard microorganism strains which are frequently seen in root canal infections, using odor data collected using an electronic nose instrument. METHOD: Our microorganism odor data set consisted of 5 repeated samples from 7 different classes at 4 concentration levels. For each concentration, 35 samples were classified using 3 different discriminant analysis methods. In order to determine an optimal setting for using electronic-nose in such an application, we have tried 3 different approaches in evaluating sensor responses. Moreover, we have used 3 different sensor baseline values in normalizing sensor responses. Since the number of sensors is relatively large compared to sample size, we have also investigated the influence of two different dimension reduction methods on classification performance. RESULTS: We have found that quadratic type discriminant analysis outperforms other varieties of this method. We have also observed that classification performance decreases as the concentration decreases. Among different baseline values used for pre-processing the sensor responses, the model where the minimum values of sensor readings in the sample were accepted as the baseline yields better classification performance. Corresponding to this optimal choice of baseline value, we have noted that among different sensor response model and feature reduction method combinations, the difference model with standard deviation based dimension reduction or normalized fractional difference model with principal component analysis based dimension reduction results in the best overall performance across different concentrations. CONCLUSION: Our results reveal that the electronic nose technology is a promising and convenient alternative for classifying microorganisms that cause root canal infections. With our comprehensive approach, we have also determined optimal settings to obtain higher classification performance using this technology and discriminant analysis.

Evaluation of microleakage of roots filled with different techniques with a computerized fluid filtration technique.

The aim of this study was to compare the apical leakage of roots filled with different materials using a computerized fluid filtration technique. There were 36 freshly extracted human maxillary central incisors selected. After preparation and irrigation, 3 experimental groups of 10 roots were constituted. Ten roots were filled with AH Plus and gutta-percha, 10 roots were filled with Sealapex and gutta-percha, and 10 roots were filled with Epiphany sealer and Resilon cone using a single cone technique. Three roots were used as a positive control and three roots were used as a negative control group. Evaluation of the apical leakages was performed with a computerized fluid filtration technique. According to the results, the difference between group 3 and 1 and group 3 and 2 was statistically significant (p<0.05). Although group 2 leaked the most, there was no significant difference between group 1 and 2.