Mallya S, Lam E: White and Pharoah's oral radiology: principles and interpretation, 8 ed., New York: Elsevier Health Sciences, 2018.

Google Scholar 

Bayati S, Keikhaei B, Bahadoram M, Mahmoudian-Sani MR, Vaneshani M, Behbahani F: Radiographic features of the maxillofacial anomalies in beta-thalassemia major: With new view. World J Plast Surg 10(3):78, 2021. https://doi.org/10.29252/wjps.10.3.78

Article  PubMed  PubMed Central  Google Scholar 

Padbury Jr AD, Tözüm TF, Taba Jr M, Ealba EL, West BT, Burney RE, et al.: The impact of primary hyperparathyroidism on the oral cavity. J Clin Endocrinol Metab 91(9):3439-45, 2006. https://doi.org/10.1210/jc.2005-2282

Article  CAS  PubMed  Google Scholar 

Antony DP, Thomas T, Nivedhitha M: Two-dimensional periapical, panoramic radiography versus three-dimensional cone-beam computed tomography in the detection of periapical lesion after endodontic treatment: A systematic review. Cureus. https://doi.org/10.7759/cureus.7736, 2020

Prakash N, Karjodkar FR, Sansare K, Sonawane HV, Bansal N, Arwade R: Visibility of lamina dura and periodontal space on periapical radiographs and its comparison with cone beam computed tomography. Contemp Clin Dent 6(1):21-5, 2015. https://doi.org/10.4103/0976-237X.149286

Article  PubMed  PubMed Central  Google Scholar 

Mupparapu M, Wu C-W, Chen Y-C: Artificial intelligence, machine learning, neural networks, and deep learning: Futuristic concepts for new dental diagnosis. Quintessence Int 49(9):687-8, 2018. https://doi.org/10.3290/j.qi.a41107

Article  PubMed  Google Scholar 

Schwendicke Fa, Samek W, Krois J: Artificial intelligence in dentistry: chances and challenges. J Dent Res 99(7):769-74, 2020. https://doi.org/10.1177/0022034520915714

Article  Google Scholar 

LeCun Y, Bengio Y, Hinton G: Deep learning. Nature 521(7553):436-44, 2015. https://doi.org/10.1038/nature14539

Article  CAS  PubMed  Google Scholar 

Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E: Deep learning applications and challenges in big data analytics, J Big Data 2:1-21, 2015. https://doi.org/10.1186/s40537-014-0007-7

Article  Google Scholar 

Pauwels R, Brasil DM, Yamasaki MC, Jacobs R, Bosmans H, Freitas DQ, et al.: Artificial intelligence for detection of periapical lesions on intraoral radiographs: Comparison between convolutional neural networks and human observers. Oral Surg Oral Med Oral Pathol Oral Radiol 131(5):610-6, 2021. https://doi.org/10.1016/j.oooo.2021.01.018

Article  PubMed  Google Scholar 

Heo MS, Kim JE, Hwang JJ, Han SS, Kim JS, Yi WJ, et al.: Artificial intelligence in oral and maxillofacial radiology: what is currently possible? Dentomaxillofac Radiol 50(3):20200375, 2021. https://doi.org/10.1259/dmfr.20200375

Article  PubMed  Google Scholar 

Ossowska A, Kusiak A, Świetlik D: Artificial intelligence in dentistry—Narrative review. Int J Environ Res Public Health 19(6):3449, 2022. https://doi.org/10.3390/ijerph19063449

Article  PubMed  PubMed Central  Google Scholar 

Seyyarer E, Ayata F, Uçkan T, Karci A: Applications and Comparisons of Optimization Algorithms Used in Convolutional Neural Networks, Comput Sci 5(2):90-8, 2020. https://doi.org/10.1109/idap.2019.8875929

Article  Google Scholar 

McHugh M: Interrater reliability: the kappa statistic. Biochem Med 22(3):276–82, 2012. https://doi.org/10.11613/bm.2012.031

Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A: The pascal visual object classes (voc) challenge. Int J Comput Vis 88:303-38, 2010. https://doi.org/10.1007/s11263-009-0275-4

Article  Google Scholar 

Saito T, Rehmsmeier M: The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. J Plos, https://doi.org/10.1371/journal.pone.0118432, 10(3):e0118432 , 2015.

Article  CAS  Google Scholar 

Harorlı A, Akgül M, Yılmaz B, Bilge O, Dağistan S, Çakur B, et al.: Ağız, Diş ve Çene Radyolojisi. 1 ed., İstanbul: Nobel Tıp Kitapevleri Tic, 2014.

Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein M: The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. Dentomaxillofac Radiol 49(1):20190107, 2020. https://doi.org/10.1259/dmfr.20190107

Article  PubMed  Google Scholar 

Jung W, Lee KE, Suh BJ, Seok H, Lee D: Deep learning for osteoarthritis classification in temporomandibular joint. Oral Dis 29(3):1050-9, 2023. https://doi.org/10.1111/odi.14056

Article  PubMed  Google Scholar 

Lee JH, Jeong SN: Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: A pilot study. Med, https://doi.org/10.1097/MD.0000000000020787, 2020.

Article  PubMed  Google Scholar 

Hiraiwa T, Ariji Y, Fukuda M, Kise Y, Nakata K, Katsumata A et al.: A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofac Radiol 48(3):20180218, 2019. https://doi.org/10.1259/dmfr.20180218

Article  PubMed  Google Scholar 

Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi JG, Gaudin R, et al.: Detecting caries lesions of different radiographic extension on bitewings using deep learning. J Dent 100:103425, 2020. https://doi.org/10.1016/j.jdent.2020.103425

Article  PubMed  Google Scholar 

Fadel HT, Abu-Hammad O, Ghulam OA, Dar-Odeh N: Are artificial neural networks useful for predicting overhanging dental restorations? a cross-sectional study. World J Dent 11(2):99-104, 2020. https://doi.org/10.5005/jp-journals-10015-1709

Article  Google Scholar 

Kyventidis N, Angelopoulos C: Surgery. Intraoral radiograph anatomical region classification using neural networks. Int J Comput Assist Radiol Surg 16:447-55, 2021. https://doi.org/10.1007/s11548-021-02321-4

Article  PubMed  Google Scholar 

Liu M-Q, Xu Z-N, Mao W-Y, Li Y, Zhang X-H, Bai H-L, et al.: Deep learning-based evaluation of the relationship between mandibular third molar and mandibular canal on CBCT. Clin Oral Investig 1–11, 2022.https://doi.org/10.1007/s00784-021-04082-5

Setzer FC, Shi KJ, Zhang Z, Yan H, Yoon H, Mupparapu M, et al.: Artificial intelligence for the computer-aided detection of periapical lesions in cone-beam computed tomographic images. J Endod 46(7):987-93, 2020. https://doi.org/10.1016/j.joen.2020.03.025

Article  PubMed  Google Scholar 

Choi H-I, Jung S-K, Baek S-H, Lim WH, Ahn S-J, Yang I-H, et al.: Artificial intelligent model with neural network machine learning for the diagnosis of orthognathic surgery. J Craniofac Surg 30(7):1986-9, 2019. https://doi.org/10.1097/scs.0000000000005650

Article  PubMed  Google Scholar 

Revilla-León M, Gómez-Polo M, Vyas S, Barmak BA, Galluci GO, Att W, et al.: Artificial intelligence applications in implant dentistry: A systematic review. J Prosthet Dent 129(2):293-300, 2023. https://doi.org/10.1016/j.prosdent.2021.05.008

Article  PubMed  Google Scholar 

van der Stelt P: Better imaging: the advantages of digital radiography. J Am Dent Assoc 139: 7–13, 2008. https://doi.org/10.14219/jada.archive.2008.0357

Benson BW, Flint DJ, Liang H, Opatowsky M: Advances in diagnostic imaging for pathologic conditions of the jaws. Head Neck Pathol 8:383-91, 2014. https://doi.org/10.1007/s12105-014-0575-z

Article  PubMed  PubMed Central  Google Scholar 

Geetha V, Aprameya K, Hinduja DM: Systems. Dental caries diagnosis in digital radiographs using back-propagation neural network. Health Inf Sci Syst 8:1-14, 2020. https://doi.org/10.1007/s13755-019-0096-y

Article  Google Scholar 

Başaran M, Akbayır Ö, Fidan M, Sertsöz M, Öztürk M: Classification of Rail Surface Defects and Rail Cracks by Convolutional Residual Network Model. Railway Engineering (19):160-70, 2024. https://doi.org/10.47072/demiryolu.1207956

Article  Google Scholar 

He K, Zhang X, Ren S, Sun J: Deep residual learning for image recognition, In Proceedings of the IEEE conference on computer vision and pattern recognition. http://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html, 2016.

Cejudo JE, Chaurasia A, Feldberg B, Krois J, Schwendicke F: Classification of dental radiographs using deep learning. J Clin Med 10(7):1496, 2021. https://doi.org/10.3390/jcm10071496

Article  PubMed  PubMed Central  Google Scholar 

Lee J-H, Kim Y-T, Lee J-B: Identification of dental implant systems from low-quality and distorted dental radiographs using AI trained on a large multi-center dataset. Sci Rep 14(1):12606, 2024. https://doi.org/10.1038/s41598-024-63422-z

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ramponi A, Plank B: Neural unsupervised domain adaptation in NLP-a surve. arXiv preprint arXiv, https://doi.org/10.48550/arXiv.2006.00632, 2020.

Franco A, Murray J, Heng D, Lygate A, Moreira D, Ferreira J, et al.: Binary decisions of artificial intelligence to classify third molar development around the legal age thresholds of 14, 16 and 18 years. Sci Rep 14(1):4668, 2024. https://doi.org/10.1038/s41598-024-55497-5

Article  CAS  PubMed