Accuracy of Periapical Radiography and Cone-Beam Computed Tomography Scans in Diagnosing Apical Periodontitis Using Histopathological Findings as a Gold Standard

Introduction: The aim of this study was to evaluate the accuracy of two imaging methods in diagnosing apical periodontitis (AP) using histopathological findings as a gold standard. Methods: The periapex of 83 treated or untreated roots of dogs` teeth was examined using periapical radiography (PR), cone-beam computed tomography (CBCT) scans, and histology. Sensitivity, specificity, predictive values, and accuracy of PR and CBCT diagnosis were calculated. Results: PR detected AP in 71% of roots, a CBCT scan detected AP in 84%, and AP was histologically diagnosed in 93% (p = 0.001). Overall, sensitivity was 0.77 and 0.91 for PR and CBCT, respectively. Specificity was 1 for both. Negative predictive value was 0.25 and 0.46 for PR and CBCT, respectively. Positive predictive value was 1 for both. Diagnostic accuracy (true positives + true negatives) was 0.78 and 0.92 for PR and CBCT (p = 0.028), respectively. Conclusion: A CBCT scan was more sensitive in detecting AP compared with PR, which was more likely to miss AP when it was still present. (J Endod 2009;35:1009-1012)

Ex Vivo Accuracy of an Apex Locator Using Digital Signal Processing in Primary Teeth

The purpose of this study was to evaluate ex vivo the accuracy an electronic apex locator during root canal length determination in primary molars. Methods: One calibrated examiner determined the root canal length in 15 primary molars (total=34 root canals) with different stages of root resorption. Root canal length was measured both visually, with the placement of a K-file 1 mm short of the apical foramen or the apical resorption bevel, and electronically using an electronic apex locator (Digital Signal Processing). Data were analyzed statistically using the intraclass correlation (ICC) test. Results: Comparing the actual and electronic root canal length measurements in the primary teeth showed a high correlation (ICC=0.95) Conclusions: The Digital Signal Processing apex locator is useful and accurate for apex foramen location during root canal length measurement in primary molars. (Pediatr Dent 200937:320-2) Received April 75, 2008 vertical bar Lost Revision August 21, 2008 vertical bar Revision Accepted August 22, 2008

Ex vivo evaluation of the accuracy of two electronic apex locators during root canal length determination in primary teeth

Aim To evaluate ex vivo the accuracy of two electronic apex locators during root canal length determination in primary incisor and molar teeth with different stages of physiological root resorption. Methodology One calibrated examiner determined the root canal length in 17 primary incisors and 16 primary molars (total of 57 root canals) with different stages of root resorption based on the actual canal length and using two electronic apex locators. Root canal length was measured both visually, with the placement of a K-file 1 mm short of the apical foramen or the apical resorption bevel, and electronically using two electronic apex locators (Root ZX II - J. Morita Corp. and Mini Apex Locator - SybronEndo) according to the manufacturers` instructions. Data were analysed statistically using the intraclass correlation (ICC) test. Results Comparison of the actual root canal length and the electronic root canal length measurements revealed high correlation (ICC = 0.99), regardless of the tooth type (single-rooted and multi-rooted teeth) or the presence/absence of physiological root resorption. Conclusions Root ZX II and Mini Apex Locator proved useful and accurate for apex foramen location during root canal length measurement in primary incisors and molars.

Accuracy of the iPex multi-frequency electronic apex locator in primary molars: an ex vivo study

P>Aim To evaluate ex vivo the accuracy of the iPex multi-frequency electronic apex locator (NSK Ltd, Tokyo, Japan) for working length determination in primary molar teeth. Methodology One calibrated examiner determined the working length in 20 primary molar teeth (total of 33 root canals). Working length was measured both visually, with the placement of a K-file 1 mm short of the apical foramen or the most coronal limit of root resorption, and electronically using the electronic apex locator iPex, according to the manufacturers` instructions. Data were analysed statistically using the intraclass correlation (ICC) test. Results Comparison of the actual and the electronic measurements revealed high correlation (ICC = 0.99) between the methods, regardless of the presence or absence of physiological root resorption. Conclusions In this laboratory study, the iPex accurately identified the apical foramen or the apical opening location for working length measurement in primary molar teeth.

Bayesian network classifiers: Beyond classification accuracy

This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing the tradeoff between the precise probability estimates produced by time consuming unrestricted Bayesian networks and the computational efficiency of Naive Bayes (NB) classifiers. The proposed approach is based on the fundamental principles of the Heuristic Search Bayesian network learning. The Markov Blanket concept, as well as a proposed ""approximate Markov Blanket"" are used to reduce the number of nodes that form the Bayesian network to be induced from data. Consequently, the usually high computational cost of the heuristic search learning algorithms can be lessened, while Bayesian network structures better than NB can be achieved. The resulting algorithms, called DMBC (Dynamic Markov Blanket Classifier) and A-DMBC (Approximate DMBC), are empirically assessed in twelve domains that illustrate scenarios of particular interest. The obtained results are compared with NB and Tree Augmented Network (TAN) classifiers, and confinn that both proposed algorithms can provide good classification accuracies and better probability estimates than NB and TAN, while being more computationally efficient than the widely used K2 Algorithm.