Publications by authors named "Renata Maira de Souza Leal"
Article Synopsis
- A study developed and validated an AI tool for automatically segmenting the pulp cavity of mandibular molars from cone-beam CT images, dividing data into training, validation, and testing sets for evaluation.
- The AI tool demonstrated high accuracy in segmentation, with Dice similarity coefficients of 88% for first molars and 90% for second molars, while also significantly reducing time needed for segmentation compared to manual methods.
- This AI-driven method can enhance efficiency in endodontic procedures by providing quick, accurate 3D models, potentially improving patient outcomes and anticipating complications.
Introduction: The anatomical configuration classified as Vertucci's type III is described as the second most prevalent in mandibular incisors.
Methods: Thirty-six Vertucci's type III mandibular incisors were evaluated by micro-computed tomography (micro-CT) and divided into 3 groups (n = 12) according to the root canal preparation protocol (HyFlex CM [HCM], HyFlex EDM [HEDM], and Sequence Rotary File [SRF]). The teeth were scanned before and after performing 0.
Objectives: This study evaluated the efficiency of WaveOne Primary files (Dentsply Sirona) for removing root canal fillings with 2 types of movement: reciprocating (RCP) and continuous counterclockwise rotation (CCR).
Materials And Methods: Twenty mandibular incisors were prepared with a RCP instrument (25.08) and filled using the Tagger hybrid obturation technique.