Atatürk Üniversitesi Atatürk Üniversitesi
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Makale Tam Makale Uluslararası 2021

Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs.

Münevver Kılıc, Ibrahim Sevki Bayrakdar, Özer Çelik, Elif Bilgir, Kaan Orhan, Ozan Barıs Aydın, Fatma Akkoca Kaplan, Hande Sağlam, Alper Odabaş, Ahmet Faruk Aslan, Ahmet Berhan Yılmaz

DENTOMAXILLOFACIAL RADIOLOGY Cilt 50 · 01.09.2021
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Özet

Objective: This study evaluated the use of a deep-learning approach for automated detection and numbering of deciduous teeth in children as depicted on panoramic radiographs.

Anahtar kelimeler

deep learning CHILDREN Panoramic radiography artificial intelligence DIAGNOSIS TEETH pediatric dentistry tooth detecting deciduous tooth CONVOLUTIONAL NEURAL-NETWORK CLASSIFICATION FUTURE

Atıf için künye

Kılıc, M., Bayrakdar, I. S., Çelik, Ö., Bilgir, E., Orhan, K., Aydın, O. B., Kaplan, F. A., Sağlam, H., Odabaş, A., Aslan, A. F., & Yılmaz, A. B. (2021). Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. DENTOMAXILLOFACIAL RADIOLOGY, 50. https://doi.org/10.1259/dmfr.20200172

BibTeX 
@article{kilic2021-10845,
  author = {Kılıc, Münevver and Bayrakdar, Ibrahim Sevki and Çelik, Özer and Bilgir, Elif and Orhan, Kaan and Aydın, Ozan Barıs and Kaplan, Fatma Akkoca and Sağlam, Hande and Odabaş, Alper and Aslan, Ahmet Faruk and Yılmaz, Ahmet Berhan},
  title = {Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs.},
  journal = {DENTOMAXILLOFACIAL RADIOLOGY},
  year = {2021},
  volume = {50},
  issn = {0250-832X},
  doi = {10.1259/dmfr.20200172},
  url = {https://akademik.atauni.edu.tr/yayinlar/10845-artificial-intelligence-system-for-automatic-deciduous-tooth-detection-and}
}