Evaluation of different color measurement methods for determining tooth color.

Aim: The purpose of this study is to develop software at a low cost that enables the detection of tooth colors by capturing photographs using various devices, and to compare its effectiveness with existing expensive methods.

Material And Methods: A total of 60 anterior central incisor teeth from 30 individuals were included in the study. The CIELAB values (L,a,b) of each tooth were measured using a spectrophotometer, which is considered the gold standard.

A fine-tuned YOLOv5 deep learning approach for real-time house number detection.

Detection of small objects in natural scene images is a complicated problem due to the blur and depth found in the images. Detecting house numbers from the natural scene images in real-time is a computer vision problem. On the other hand, convolutional neural network (CNN) based deep learning methods have been widely used in object detection in recent years.

Developing deep learning methods for classification of teeth in dental panoramic radiography.

Objectives: We aimed to develop an artificial intelligence-based clinical dental decision-support system using deep-learning methods to reduce diagnostic interpretation error and time and increase the effectiveness of dental treatment and classification.

Study Design: We compared the performance of 2 deep-learning methods, You Only Look Once V4 (YOLO-V4) and Faster Regions with the Convolutional Neural Networks (R-CNN), for tooth classification in dental panoramic radiography for tooth classification in dental panoramic radiography to determine which is more successful in terms of accuracy, time, and detection ability. Using a method based on deep-learning models trained on a semantic segmentation task, we analyzed 1200 panoramic radiographs selected retrospectively.