Development of Periapical Index Score Classification System in Periapical Radiographs Using Deep Learning

Arias Z, Nizami MZI, Chen X, Xu B, Kuang C, Omori K, Takashiba S: Recent advances in apical periodontitis treatment: a narrative review. Bioengineering, https://doi.org/10.3390/bioengineering10040488, April 19, 2023.

Article  PubMed  PubMed Central  Google Scholar 

Tibúrcio-Machado CS, Michelon C, Zanatta FB, Gomes MS, Marin JA, Bier CA: The global prevalence of apical periodontitis: a systematic review and meta-analysis. Int Endod J, https://doi.org/10.1111/iej.13467, Jan 22, 2021.

Article  PubMed  Google Scholar 

Loesche WJ: Medical microbiology, 4th edition, Galveston: University of Texas Medical Branch at Galveston; 1996.

Google Scholar 

Zero DT, Zandona AF, Vail MM, Spolnik KJ: Dental caries and pulpal disease. Dent Clin North Am, 55(1):29–46, 2011.

Article  PubMed  Google Scholar 

Tanomaru-Filho M, Jorge EG, Duarte MA, Gonçalves M, Guerreiro-Tanomaru JM: Comparative radiographic and histological analyses of periapical lesion development. Oral Surg Oral Med Oral Pathol Oral Radiol Endod, 107(3):442-447, 2009.

Article  PubMed  Google Scholar 

Ørstavik D, Kerekes K, Eriksen HM: The periapical index: a scoring system for radiographic assessment of apical periodontitis. Dent Traumatol, 2(1):20–34, 1986.

Article  Google Scholar 

Panyarak W, Wantanajittikul K, Suttapak W, Charuakkra A, Prapayasatok S: Feasibility of deep learning for dental caries classification in bitewing radiographs based on the ICCMS™ radiographic scoring system. Oral Surg Oral Med Oral Pathol Oral Radiol, https://doi.org/10.1016/j.oooo.2022.06.012, July 2, 2022.

Article  PubMed  Google Scholar 

Suttapak W, Panyarak W, Jira-apiwattana D, Wantanajittikul K: A unified convolution neural network for dental caries classification. ECTI Trans Comput Inf Technol ECTI-CIT, https://doi.org/10.37936/ecti-cit.2022162.245901, June 4, 2022.

Article  Google Scholar 

Sadr S, Mohammad-Rahimi H, Motamedian SR, Zahedrozegar S, Motie P, Vinayahalingam S, Dianat O, Nosrat A: Deep learning for detection of periapical radiolucent lesions: a systematic review and meta-analysis of diagnostic test accuracy. J Endod, https://doi.org/10.1016/j.joen.2022.12.007, December 21, 2022.

Article  PubMed  Google Scholar 

Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data, https://doi.org/10.1186/s40537-021-00444-8, March 31, 2021.

Article  PubMed  PubMed Central  Google Scholar 

Krizhevsky A, Sutskever I, Hinton GE: ImageNet classification with deep convolutional neural networks. Proceedings of the 26th annual conference on neural information processing systems 2012, 60(6): 84–90, 2012.

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A: Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on computer vision and pattern recognition, 1–9, 2015.

He K, Zhang X, Ren S, Sun J: Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on computer vision and pattern recognition, 770–778, 2016.

Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez CI: A survey on deep learning in medical image analysis. Med Image Anal, https://doi.org/10.1016/j.media.2017.07.005, July 26, 2017.

Article  PubMed  Google Scholar 

Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, https://doi.org/10.1001/jama.2016.17216, December 13, 2016.

Article  PubMed  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, https://doi.org/10.1016/j.compbiomed.2016.11.0032017, January 1, 2017.

Article  PubMed  Google Scholar 

Al-Ghamdi ASA, Ragab M, AlGhamdi SA, Asseri AH, Mansour RF, Koundal D: Detection of dental diseases through X-ray images using neural search architecture network. Comput Intell Neurosci, https://doi.org/10.1155/2022/3500552, April 30, 2022.

Article  PubMed  PubMed Central  Google Scholar 

Mao YC, Chen TY, Chou HS, Lin SY, Liu SY, Chen YA, Liu YL, Chen CA, Huang YC, Chen SL, Li CW, Abu PAR, Chiang WY: Caries and restoration detection using bitewing film based on transfer learning with CNNs. Sensors (Basel), https://doi.org/10.3390/s21134613, Jul 5, 2021.

Article  PubMed  PubMed Central  Google Scholar 

Rajasekhar R, Soman S, Sebastian VM, Muliyar S, Cherian NM: Indexes for periapical health evaluation: a review. Int Dent Res, 2022;12(2):97-106.

Article  Google Scholar 

Maia Filho EM, Calisto AM, De Jesus Tavarez RR, de Castro Rizzi C, Bezerra Segato RA, Bezerra da Silva LA: Correlation between the periapical index and lesion volume in cone-beam computed tomography images, Iran Endod J, 2018 Spring;13(2):155–158.

Moidu NP, Sharma S, Chawla A, Kumar V, Logani A: Deep learning for categorization of endodontic lesion based on radiographic periapical index scoring system. Clin Oral Investig, https://doi.org/10.1007/s00784-021-04043-y, July 2, 2021.

Article  PubMed  Google Scholar 

Issa J, Jaber M, Rifai I, Mozdziak P, Kempisty B, Dyszkiewicz-Konwińska M: Diagnostic test accuracy of artificial intelligence in detecting periapical periodontitis on two-dimensional radiographs: a retrospective study and literature review. Medicina (Kaunas), https://doi.org/10.3390/medicina59040768, April 15, 2023.

Article  PubMed  Google Scholar 

Bachani L, Singh M, Anshul, Lingappa A: Ideal radiographs: an insight. IP Int J Maxillofac Imaging, 6(3):56–64, 2020.

Article  Google Scholar 

Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D: Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the 2017 IEEE International conference on computer vision, 618–626, 2017.

Ying X: An overview of overfitting and its solutions. J Phys Conf Ser, 1168(2):022022, 2019.

Article  Google Scholar 

Mooijman P, Catal C, Tekinerdogan B, Lommen A, Blokland M: The effects of data balancing approaches: a case study. Appl Soft Comput, 132:109853, 2023.

Article  Google Scholar 

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