Jihoon Kim1, Jae Joon Hwang2,3, Taesung Jeong3,1, Bong-Hae Cho2,3, Jonghyun Shin3,1. 1. Department of Pediatric Dentistry, School of Dentistry, Pusan National University, Yangsan, South Korea. 2. Department of Oral and Maxillofacial Radiology, School of Dentistry, Pusan National University, Yangsan, South Korea. 3. Dental and Life Science Institute & Dental Research Institute, School of Dentistry, Pusan National University, Yangsan, South Korea.
Abstract
OBJECTIVES: The purpose of this study is to develop and evaluate the performance of a model that automatically sets a region of interest (ROI) and diagnoses mesiodens in panoramic radiographs of growing children using deep learning technology. METHODS: Out of 988 panoramic radiographs, 489 patients with mesiodens were classified as an experimental group, and 499 patients without mesiodens were classified as a control group. This study consists of two networks. The first network (DeeplabV3plus) is a segmentation model that uses the posterior molar space to set the ROI in the maxillary anterior region with the mesiodens in the panoramic radiograph. The second network (Inception-resnet-v2) is a classification model that uses cropped maxillary anterior teeth to determine the presence of mesiodens. The data were divided into five groups and cross-validated. Deep learning model were created and trained using Inception-ResNet-v2. The performance of the segmentation network was evaluated using accuracy, Intersection over Union (IoU), and MeanBFscore. The overall network performance was evaluated using accuracy, precision, recall, and F1-score. RESULTS: Segmentation performance using posterior molar space in panoramic radiographs was 0.839, IoU 0.762, and MeanBFscore 0.907. The mean values of accuracy, precision, recall, F1-score, and area under the curve for the diagnosis of mesiodens using automatic segmentation were 0.971, 0.971, 0.971, 0.971, and 0.971, respectively. CONCLUSIONS: The diagnostic performance of the deep learning system using posterior molar space on the panoramic radiograph was sufficiently useful. The results of the deep learning system confirmed the possibility of complete automation of the classification of mesiodens.
OBJECTIVES: The purpose of this study is to develop and evaluate the performance of a model that automatically sets a region of interest (ROI) and diagnoses mesiodens in panoramic radiographs of growing children using deep learning technology. METHODS: Out of 988 panoramic radiographs, 489 patients with mesiodens were classified as an experimental group, and 499 patients without mesiodens were classified as a control group. This study consists of two networks. The first network (DeeplabV3plus) is a segmentation model that uses the posterior molar space to set the ROI in the maxillary anterior region with the mesiodens in the panoramic radiograph. The second network (Inception-resnet-v2) is a classification model that uses cropped maxillary anterior teeth to determine the presence of mesiodens. The data were divided into five groups and cross-validated. Deep learning model were created and trained using Inception-ResNet-v2. The performance of the segmentation network was evaluated using accuracy, Intersection over Union (IoU), and MeanBFscore. The overall network performance was evaluated using accuracy, precision, recall, and F1-score. RESULTS: Segmentation performance using posterior molar space in panoramic radiographs was 0.839, IoU 0.762, and MeanBFscore 0.907. The mean values of accuracy, precision, recall, F1-score, and area under the curve for the diagnosis of mesiodens using automatic segmentation were 0.971, 0.971, 0.971, 0.971, and 0.971, respectively. CONCLUSIONS: The diagnostic performance of the deep learning system using posterior molar space on the panoramic radiograph was sufficiently useful. The results of the deep learning system confirmed the possibility of complete automation of the classification of mesiodens.
Entities:
Keywords:
artificial intelligence; deep learning; mesiodens; posterior molar space; radiography