Diagnostic charting of panoramic radiography using deep-learning artificial intelligence system

Fukuda M, Inamoto K, Shibata N, Ariji Y, Yanashita Y, Kutsuna S, Nakata K, Katsumata A, Fujita H, Ariji E. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiol. 2020;36(4):337–43.

Article 

Google Scholar 

Stuart C, White P, Michael J. Oral radiology: principles and interpretation. Mosby. India: Elsevier; 2014.

Google Scholar 

Perschbacher S. Interpretation of panoramic radiographs. Aust Dent J. 2012;57Suppl1:40–5.

Article 

Google Scholar 

European Society of Radiology (ESR). What the radiologist should know about artificial intelligence—an ESR white paper. Insights Imaging. 2019;10(1):44.

Article 

Google Scholar 

Hiraiwa T, Ariji Y, Fukuda M, Kise Y, Nakata K, Katsumata A, Fujita H, Ariji E. A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofac Radiol. 2019;48(3):20180218.

Article 

Google Scholar 

Ariji Y, Yanashita Y, Kutsuna S, Muramatsu C, Fukuda M, Kise Y, Nozawa M, Kuwada C, Fujita H, Katsumata A, Ariji E. Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique. Oral Surg Oral Med Oral Pathol Oral Radiol. 2019;128(4):424–30.

Article 

Google Scholar 

Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: a systematic review. Dentomaxillofac Radiol. 2020;49(1):20190107.

Article 

Google Scholar 

Tuzoff DV, Tuzova LN, Bornstein MM, Krasnov AS, Kharchenko MA, Nikolenko SI, Sveshnikov MM, Bednenko GB. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofac Radiol. 2019;48(4):20180051.

Article 

Google Scholar 

Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent. 2018;77:106–11.

Article 

Google Scholar 

Sen D, Chakrabarti R, Chatterjee S, Grewal DS, Manrai K. Artificial intelligence and the radiologist: the future in the Armed Forces Medical Services. BMJ Mil Health. 2020;166(4):254–6.

PubMed 

Google Scholar 

Ueda D, Shimazaki A, Miki Y. Technical and clinical overview of deep learning in radiology. Jpn J Radiol. 2019;37(1):15–33. https://doi.org/10.1007/s11604-018-0795-3.

Article 
PubMed 

Google Scholar 

Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp. 2018;2(1):35.

Article 

Google Scholar 

Langlotz CP (2019) Will artificial intelligence replace radiologists? Radiological Society of North America, Radiology: artificial intelligence 1(3):e190058.

Nagi R, Aravinda K, Rakesh N, Gupta R, Pal A, Mann AK. Clinical applications and performance of intelligent systems in dental and maxillofacial radiology: a review. Imaging Sci Dent. 2020;50(2):81–92.

Article 

Google Scholar 

Abdalla-Aslan R, Yeshua T, Kabla D, Leichter I, Nadler C. An artificial intelligence system using machine-learning for automatic detection and classification of dental restorations in panoramic radiography. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020;130(5):593–602.

Article 

Google Scholar 

Miki Y, Muramatsu C, Hayashi T, Zhou X, Hara T, Katsumata A, Fujita H. Classification of teeth in cone-beam CT using deep convolutional neural network. Comput Biol Med. 2017;80:24–9.

Article 

Google Scholar 

Chen H, Zhang K, Lyu P, Li H, Zhang L, Wu J, Lee CH. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Sci Rep. 2019;9(1):3840.

Article 

Google Scholar 

Kuang W, Ye W. A kernel-modified SVM based computeraided diagnosis system in initial caries. In: IITA‘08 Proceedings of the Second International Symposium on Intelligent Information Technology Application, IEEE, Shanghai, China. 2008. p. 20–22.

Devito KL, de Souza BF, Felippe Filho WN. An artificial multilayer perceptron neural network for diagnosis of proximal dental caries. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2008;106(6):879–84.

Article 

Google Scholar 

Takahashi T, Nozaki K, Gonda T, Mameno T, Wada M, Ikebe K. Identification of dental implants using deep learning-pilot study. Int J Implant Dent. 2020;6(1):53.

Article 

Google Scholar 

Lin P, Lai Y, Huang PJPR. An effective classification and numbering system for dental bitewing radiographs using teeth region and contour information. Pattern Recognit. 2010;43(4):1380–92.

Article 

Google Scholar 

Poedjiastoeti W, Suebnukarn S. Application of convolutional neural network in the diagnosis of jaw tumors. Healthc Inform Res. 2018;24(3):236–41.

Article 

Google Scholar 

Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, Schwendicke F. Deep learning for the radiographic detection of apical lesions. J Endod. 2019;45(7):917-922.e5.

Article 

Google Scholar 

Khan HA, Haider MA, Ansari HA, Ishaq H, Kiyani A, Sohail K, Muhammad M, Khurram SA. Automated feature detection in dental periapical radiographs by using deep learning. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020. https://doi.org/10.1016/j.oooo.2020.08.024 (Epub ahead of print).

Article 
PubMed 

Google Scholar 

Kuwada C, Ariji Y, Fukuda M, Kise Y, Fujita H, Katsumata A, Ariji E. Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020;130(4):464–9.

Article 

Google Scholar 

Orhan K, Bilgir E, Bayrakdar IS, Ezhov M, Gusarev M, Shumilov E. Evaluation of artificial intelligence for detecting impacted third molars on cone-beam computed tomography scans. J Stomatol Oral Maxillofac Surg. 2020;S2468–7855(20):30303–7.

Google Scholar