Ibrahim Sevki Bayrakdar1,2, Kaan Orhan3,4, Serdar Akarsu5, Özer Çelik5,4, Samet Atasoy6, Adem Pekince7, Yasin Yasa8, Elif Bilgir9, Hande Sağlam9, Ahmet Faruk Aslan5, Alper Odabaş5. 1. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26240, Eskisehir, Turkey. ibrahimsevkibayrakdar@gmail.com. 2. Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir, Turkey. ibrahimsevkibayrakdar@gmail.com. 3. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey. 4. Ankara University Medical Design Application and Research Center (MEDITAM), Ankara, Turkey. 5. Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey. 6. Department of Restorative Dentistry, Faculty of Dentistry, Ordu University, Ordu, Turkey. 7. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Karabuk University, Karabuk, Turkey. 8. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ordu University, Ordu, Turkey. 9. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26240, Eskisehir, Turkey.
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.
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.
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