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Title: | Deep-learning approach for caries detection and segmentation on dental bitewing radiographs |
Authors: | Bayrakdar, Ibrahim Sevki Orhan, Kaan Akarsu, Serdar Celik, Ozer Atasoy, Samet Pekince, Adem Yasa, Yasin Bilgir, Elif Saglam, Hande Aslan, Ahmet Faruk Odabas, Alper Ordu Üniversitesi 0000-0002-6598-8262 0000-0001-9521-4682 0000-0002-7816-1635 0000-0002-4409-3101 0000-0001-7792-5106 0000-0001-6768-0176 0000-0001-5036-9867 Yasa |
Keywords: | NEURAL-NETWORK Artificial intelligence; Deep learning; Tooth caries; Bitewing radiographs; Dentistry |
Issue Date: | 2021 |
Publisher: | SPRINGER NEW YORK |
Citation: | Bayrakdar, IS., Orhan, K., Akarsu, S., Celik, O., Atasoy, S., Pekince, A., Yasa, Y., Bilgir, E., Saglam, H., Aslan, AF., Odabas, A. (). Deep-learning approach for caries detection and segmentation on dental bitewing radiographs. Oral Radiology, , -.Doi:10.1007/s11282-021-00577-9 |
Abstract: | Objectives The aim of this study is to recommend an automatic caries detection and segmentation model based on the Convolutional Neural Network (CNN) algorithms in dental bitewing radiographs using VGG-16 and U-Net architecture and evaluate the clinical performance of the model comparing to human observer. Methods A total of 621 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system (CranioCatch, Eskisehir, Turkey) for the detection and segmentation of caries lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Ordu University. VGG-16 and U-Net implemented with PyTorch models were used for the detection and segmentation of caries lesions, respectively. Results The sensitivity, precision, and F-measure rates for caries detection and caries segmentation were 0.84, 0.81; 0.84, 0.86; and 0.84, 0.84, respectively. Comparing to 5 different experienced observers and AI models on external radiographic dataset, AI models showed superiority to assistant specialists. Conclusion CNN-based AI algorithms can have the potential to detect and segmentation of dental caries accurately and effectively in bitewing radiographs. AI algorithms based on the deep-learning method have the potential to assist clinicians in routine clinical practice for quickly and reliably detecting the tooth caries. The use of these algorithms in clinical practice can provide to important benefit to physicians as a clinical decision support system in dentistry. |
Description: | WoS Categories : Dentistry, Oral Surgery & Medicine Web of Science Index : Science Citation Index Expanded (SCI-EXPANDED) Research Areas : Dentistry, Oral Surgery & Medicine |
URI: | http://dx.doi.org/10.1007/s11282-021-00577-9 https://www.webofscience.com/wos/woscc/full-record/WOS:000721401600001 https://pubmed.ncbi.nlm.nih.gov/34807344 http://earsiv.odu.edu.tr:8080/xmlui/handle/11489/3648 |
ISBN: | 0911-6028 1613-9674 |
Appears in Collections: | Ağız, Diş ve Çene Radyolojisi |
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