دورية أكاديمية
Performance comparison of three deep learning models for impacted mesiodens detection on periapical radiographs.
| العنوان: | Performance comparison of three deep learning models for impacted mesiodens detection on periapical radiographs. |
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| المؤلفون: | Jeon KJ; Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea., Ha EG; Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea., Choi H; Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea., Lee C; Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea., Han SS; Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea. sshan@yuhs.ac. |
| المصدر: | Scientific reports [Sci Rep] 2022 Sep 13; Vol. 12 (1), pp. 15402. Date of Electronic Publication: 2022 Sep 13. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: London : Nature Publishing Group, copyright 2011- |
| مواضيع طبية MeSH: | Deep Learning*, Adolescent ; Algorithms ; Child ; Child, Preschool ; Humans ; Radiography |
| مستخلص: | This study aimed to develop deep learning models that automatically detect impacted mesiodens on periapical radiographs of primary and mixed dentition using the YOLOv3, RetinaNet, and EfficientDet-D3 algorithms and to compare their performance. Periapical radiographs of 600 pediatric patients (age range, 3-13 years) with mesiodens were used as a training and validation dataset. Deep learning models based on the YOLOv3, RetinaNet, and EfficientDet-D3 algorithms for detecting mesiodens were developed, and each model was trained 300 times using training (540 images) and validation datasets (60 images). The performance of each model was evaluated based on accuracy, sensitivity, and specificity using 120 test images (60 periapical radiographs with mesiodens and 60 periapical radiographs without mesiodens). The accuracy of the YOLOv3, RetinaNet, and EfficientDet-D3 models was 97.5%, 98.3%, and 99.2%, respectively. The sensitivity was 100% for both the YOLOv3 and RetinaNet models and 98.3% for the EfficientDet-D3 model. The specificity was 100%, 96.7%, and 95.0% for the EfficientDet-D3, RetinaNet, and YOLOv3 models, respectively. The proposed models using three deep learning algorithms to detect mesiodens on periapical radiographs showed good performance. The EfficientDet-D3 model showed the highest accuracy for detecting mesiodens on periapical radiographs. (© 2022. The Author(s).) |
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| تواريخ الأحداث: | Date Created: 20220913 Date Completed: 20220915 Latest Revision: 20221114 |
| رمز التحديث: | 20240829 |
| مُعرف محوري في PubMed: | PMC9470664 |
| DOI: | 10.1038/s41598-022-19753-w |
| PMID: | 36100696 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 2045-2322 |
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| DOI: | 10.1038/s41598-022-19753-w |