Effect of data size on tooth numbering performance via artificial intelligence using panoramic radiographs.

Objective: This study aims to investigate the effect of number of data on model performance, for the detection of tooth numbering problem on dental panoramic radiographs, with the help of image processing and deep learning algorithms.

Study Design: The data set consists of 3000 anonymous dental panoramic X-rays of adult individuals. Panoramic X-rays were labeled on the basis of 32 classes in line with the FDI tooth numbering system.

Proposing a CNN Method for Primary and Permanent Tooth Detection and Enumeration on Pediatric Dental Radiographs.

Objective: In this paper, we aimed to evaluate the performance of a deep learning system for automated tooth detection and numbering on pediatric panoramic radiographs.

Study Design: YOLO V4, a CNN (Convolutional Neural Networks) based object detection model was used for automated tooth detection and numbering. 4545 pediatric panoramic X-ray images, processed in labelImg, were trained and tested in the Yolo algorithm.