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- 저자 : Sara Lopes (검색결과 2 건)
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da Silva Emmanuel João Nogueira Leal,de Moura Sara Gomes,de Lima Carolina Oliveira,Barbosa Ana Flávia Almeida,Misael Waleska Florentino,Lacerda Mariane Floriano Lopes Santos,Sassone Luciana Moura 대한치과보존학회 2021 Restorative Dentistry & Endodontics Vol.46 No.2
Objectives: The aim of this study was to evaluate the shaping ability of the TruShape and Reciproc Blue systems and the apical extrusion of debris after root canal instrumentation. The ProTaper Universal system was used as a reference for comparison. Materials and Methods: Thirty-three mandibular premolars with a single canal were scanned using micro-computed tomography and were matched into 3 groups (n = 11) according to the instrumentation system: TruShape, Reciproc Blue and ProTaper Universal. The teeth were accessed and mounted in an apparatus with agarose gel, which simulated apical resistance provided by the periapical tissue and enabled the collection of apically extruded debris. During root canal preparation, 2.5% sodium hypochlorite was used as an irrigant. The samples were scanned again after instrumentation. The percentage of unprepared area, removed dentin, and volume of apically extruded debris were analyzed. The data were analyzed using 1-way analysis of variance and the Tukey test for multiple comparisons at a 5% significance level. Results: No significant differences in the percentage of unprepared area were observed among the systems (p > 0.05). ProTaper Universal presented a higher percentage of dentin removal than the TruShape and Reciproc Blue systems (p < 0.05). The systems produced similar volumes of apically extruded debris (p > 0.05). Conclusions: All systems caused apically extruded debris, without any significant differences among them. TruShape, Reciproc Blue, and ProTaper Universal presented similar percentages of unprepared area after root canal instrumentation; however, ProTaper Universal was associated with higher dentin removal than the other systems.
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Pedro R. F. Rende,Joel Machado Pires,Kátia Sakimi Nakadaira,Sara Lopes,João Vale,Fabio Hecht,Fabyan E. L. Beltrão,Gabriel J. R. Machado,Edna T. Kimura,Catarina Eloy,Helton E. Ramos 대한병리학회 2024 Journal of Pathology and Translational Medicine Vol.58 No.3
Background: Among other structures, nuclear grooves are vastly found in papillary thyroid carcinoma (PTC). Considering that the application of artificial intelligence in thyroid cytology has potential for diagnostic routine, our goal was to develop a new supervised convolutional neural network capable of identifying nuclear grooves in Diff-Quik stained whole-slide images (WSI) obtained from thyroid fineneedle aspiration. Methods: We selected 22 Diff-Quik stained cytological slides with cytological diagnosis of PTC and concordant histological diagnosis. Each of the slides was scanned, forming a WSI. Images that contained the region of interest were obtained, followed by pre-formatting, annotation of the nuclear grooves and data augmentation techniques. The final dataset was divided into training and validation groups in a 7:3 ratio. Results: This is the first artificial intelligence model based on object detection applied to nuclear structures in thyroid cytopathology. A total of 7,255 images were obtained from 22 WSI, totaling 7,242 annotated nuclear grooves. The best model was obtained after it was submitted 15 times with the train dataset (14th epoch), with 67% true positives, 49.8% for sensitivity and 43.1% for predictive positive value. Conclusions: The model was able to develop a structure predictor rule, indicating that the application of an artificial intelligence model based on object detection in the identification of nuclear grooves is feasible. Associated with a reduction in interobserver variability and in time per slide, this demonstrates that nuclear evaluation constitutes one of the possibilities for refining the diagnosis through computational models.
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