Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/57615
Title: AI-Driven localization of all impacted teeth and prediction of winter angulation for third molars on panoramic radiographs: Clinical user interface design
Authors: Zirek, T.
Öziç, M.Ü.
Tassoker, M.
Keywords: Deep learning
Detection
Impacted teeth
Winter classification
YOLOv8
Deep learning
Graphical user interfaces
Learning algorithms
Radiography
Deep learning
Detection
F1 scores
Impacted tooth
Intelligence models
Localisation
Molar teeth
Panoramic radiograph
Winter classification
YOLOv8
Classification (of information)
anatomical concepts
Article
artificial intelligence
classification algorithm
clinical decision support system
clinical evaluation
controlled study
data base
deep learning
dental education
dental practice
dental restoration
dentistry
diagnostic accuracy
false negative result
human
information processing
major clinical study
mandibular second molar
panoramic radiography
performance indicator
predictive model
recall
retrospective study
second molar
simulation training
third molar
tooth impaction
transfer of learning
winter method
Publisher: Elsevier Ltd
Abstract: Purpose: Impacted teeth are abnormal tooth disorders under the gums or jawbone that cannot take their normal position even though it is time to erupt. This study aims to detect all impacted teeth and to classify impacted third molars according to the Winter method with an artificial intelligence model on panoramic radiographs. Methods: In this study, 1197 panoramic radiographs from the dentistry faculty database were collected for all impacted teeth, and 1000 panoramic radiographs were collected for Winter classification. Some pre-processing methods were performed and the images were doubled with data augmentation. Both datasets were randomly divided into 80% training, 10% validation, and 10% testing. After transfer learning and fine-tuning processes, the two datasets were trained with the YOLOv8 deep learning algorithm, a high-performance artificial intelligence model, and the detection of impacted teeth was carried out. The results were evaluated with precision, recall, mAP, and F1-score performance metrics. A graphical user interface was designed for clinical use with the artificial intelligence weights obtained as a result of the training. Results: For the detection of impacted third molar teeth according to Winter classification, the average precision, average recall, and average F1 score were obtained to be 0.972, 0.967, and 0.969, respectively. For the detection of all impacted teeth, the average precision, average recall, and average F1 score were obtained as 0.991, 0.995, and 0.993, respectively. Conclusion: According to the results, the artificial intelligence-based YOLOv8 deep learning model successfully detected all impacted teeth and the impacted third molar teeth according to the Winter classification system. © 2024
URI: https://doi.org/10.1016/j.compbiomed.2024.108755
https://hdl.handle.net/11499/57615
ISSN: 0010-4825
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Teknoloji Fakültesi Koleksiyonu

Show full item record



CORE Recommender

Page view(s)

26
checked on Aug 24, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.