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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 |
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