Texture-Based Neural Network Model for Biometric Dental Applications.

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 ( = 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.

Emergence angle: Comprehensive analysis and machine learning prediction for clinical application.

Purpose: 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.

Optimization of milling condition of composite resin blocks for CAD/CAM to improve surface roughness and flexural strength.

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.