To analyze and compare the emergence angle (EA) using two measurement methods, conventional and modified (EA-GPT and EA-R), the EAs of all-natural teeth were evaluated and classified to derive a suitable and predictable clinically applicable measurement method. Methods: Natural human teeth (n=600) were classified, cleaned, and thoroughly inspected. Teeth were scanned using an intraoral scanner. The scanned data were analyzed using three-dimensional analysis software for both methods with several points per surface. A Bland-Altman analysis was used for statistical analysis and a heat map and a nonparametric density plot to assess the repetition and distribution. An XGBoost regression model was used for prediction. Results:The EA-R method showed significantly different values compared to the EA-GPT method, representing an increase of 17.5-20.7% for the proximal surfaces. An insignificant difference between the two methods was observed for other surfaces. Different teeth classes showed variation in the normal range, thereby resulting in a new classification of the EA for all-natural teeth based on the interquartile range. The machine learning gradient boosting model predicted conventional data with an average mean absolute error of 0.9. Conclusions: Variations in the natural teeth EA and measurement methods, suggest a new classification for EA. The established artificial intelligence method demonstrated robust performance, which could aid in implementing EA measurement in prosthetic designs.

Background: The aim is to classify dentition using a novel texture-based automated convolutional neural network (CNN) for forensic and prosthetic applications. Methods: Natural human teeth (n = 600) were classified, cleaned, and inspected for exclusion criteria. The teeth were scanned with an intraoral scanner and identified using a texture-based CNN in three steps. First, through preprocessing, teeth images were segmented by extracting the front-facing region of the teeth. Then, texture features were extracted from the segmented teeth images using the discrete wavelet transform (DWT) method. Finally, deep learning-based enhanced CNN models were used to identify these images. Several experiments were conducted using five different CNN models with various batch sizes and epochs, with and without augmented data. Results: Based on experiments with five different CNN models, the highest accuracy achieved was 0.8 and the precision was 0.8 with a loss value of 0.9, a batch size of 32, and 250 epochs. A comparison of deep learning models with different parameters showed varied accuracy between the different classes of teeth. Conclusion: The accuracy of the point-based CNN method was promising. This texture-identification method will pave the way for many forensic and prosthodontic applications and will potentially help improve the precision of dental biometrics.

This study aims to elucidate the relationships between the flexural strength and surface topography of composite resin blocks, utilized for the CAD/CAM system, after milling in various step-over amounts. The rectangular specimens were milled from CAD/ CAM blocks with step-over amount 0.01, 0.02, 0.05, 0.1, and 0.2 mm; further, a three-point bending test was conducted to reveal the flexural strength. The surface morphology after milling was assessed by a 3D laser microscope. The surface roughness significantly decreased by reducing the step-over amount. Although there was significant association between the surface roughness and flexural strength by the Pearson correlation, the 95% confidence intervals of the flexural strength were between the mirror-polished and sand-blasted groups. These results suggest that a precise step-over amount enables us to obtain a smooth surface. Furthermore, the flexural strength of the rough surface milled by a large step-over amount caused no damage to the composite resin for CAD/CAM crown.