Although the connection of implant abutments to implant analogs and implant bodies has been evaluated separately, comparative evaluation of connections to implant analogs and implant bodies has not been evaluated. The objective of this study was to identify whether torque applied during laboratory manufacturing and during restoration cementation affects the compatibility between the implant body-abutment and the implant analog-abutment, as well as to spot any structural differences. Three different implant system with internal conical connection (Bioinfinity, Dant, and Dentist) were used. For each group, straight abutments (n=20) were tightened to implant analogs (n=10) and implant bodies (n=10) with hand tightening and using torque wrench device. The fitting of componenets were evaluated for each group using linear and volumetric micro-CT analysis. For linear analysis, when the mean differences (difference between measurements of the linear distance of abutments during hand tightening and applying torque) values between implant analog and implant body were compared within groups, There was no statistically significant difference observed in all groups (p> 0.05). Throughout the fabrication and clinical phases, it is recommended to tighten the abutments on the analogs with a torque wrench instrument to minimize the risk of prosthetic incompatibility.

Objectives: The purpose of the study was to evaluate the effectiveness of dental caries segmentation on the panoramic radiographs taken from children in primary dentition, mixed dentition, and permanent dentition with an Artificial Intelligence (AI) models developed using the deep learning method. Methods: This study used 6075 panoramic radiographs taken from children aged between 4 and 14 to develop the AI model. The radiographs included in the study were divided into three groups as primary dentition (n: 1857), mixed dentition (n: 1406), and permanent dentition (n: 2812). U-Net model implemented with PyTorch library was used for segmentation of caries lesions. Confusion matrix was used to evaluation of model performance. Results: In the primary dentition group, the sensitivity, precision and F1 scores calculated using the confusion matrix were found to be 0.8525, 0.9128, and 0.8816, respectively. In the mixed dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found as 0.7377, 0.9192, and 0.8185, respectively. In the permanent dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found as 0.8271, 0.9125, and 0.8677, respectively. In the total group including primary, mixed, and permanent dentition, the sensitivity, precision and F1 score calculated using the confusion matrix were 0.8269, 0.9123 and 0.8675, respectively. Conclusion: Deep learning-based AI models are promising tools for the detection and diagnosis of caries in panoramic radiographs taken from children in different dentitions.