ABSTRACT
Introduction: An in vitro comparative analysis was performed to calculate the push-out bond strength of commercially existing root repairing cements like glass ionomer cement (GIC), biodentine, mineral trioxide aggregate (MTA), and endosequence root repair material (RRM) employed in furcation perforation, with or without blood contamination present. Materials and Methods: Eighty molars were selected and subjected to furcal perforations. They were categorized based on the cement used for repair (GIC, MTA, biodentine, and endosequence RRM); furthermore, they were sub-divided into two sub-groups, that is, blood contaminated and non-contaminated. For 24 hours, all the samples were kept in an incubator till the materials were fully set. Then these samples were examined for push-out bond strength measurement. Results: The 24-hour push-out bond strength of was the highest in biodentine and the lowest in glass ionomer cement. The push-out bond strength of endosequence RRM, MTA, and GIC was influenced by blood contamination. Conclusion: The push-out bond strength of biodentine was the highest as compared to endosequence RRM, MTA angelus, and GIC. The push-out bond strength of endosequence RRM and MTA angelus after 24 hours with or without blood contamination showed insignificant differences. Group 1A (GIC contaminated with blood) displayed the least push-out bond strength among other groups.
ABSTRACT
Artificial intelligence (AI) technology has mostly been used by dental practitioners to diagnose problems, plan treatments, make clinical judgments, and predict outcomes. In endodontics, convolutional neural networks and artificial neural networks, two types of (AI) models, have been used to study the anatomy of the root canal system, measure the length of root canal, identify periapical pathology and root fractures, prediction of success of retreatment procedures, and dental pulp stem cells viability. The goal of this review is to assess AI's role in conservative dentistry and endodontics.
