Evaluation of root canal filling length on periapical radiograph using artificial intelligence

Ng YL, Mann V, Gulabivala K. A prospective study of the factors affecting outcomes of nonsurgical root canal treatment: part 1: periapical health. Int Endod J. 2011;44(7):583–609.

Article  PubMed  Google Scholar 

Ng YL, Mann V, Gulabivala K. Outcome of secondary root canal treatment: a systematic review of the literature. Int Endod J. 2008;41(12):1026–46.

Article  PubMed  Google Scholar 

Lost C. Quality guidelines for endodontic treatment: consensus report of the European society of endodontology. Int Endod J. 2006;39(12):921–30.

Article  Google Scholar 

Ng YL, et al. Outcome of primary root canal treatment: systematic review of the literature-part 2. Influence of clinical factors. Int Endod J. 2008;41(1):6–31.

Article  PubMed  Google Scholar 

Cantu AG, et al. Detecting caries lesions of different radiographic extension on bitewings using deep learning. J Dent. 2020;100:103425.

Article  PubMed  Google Scholar 

Celik B, Celik ME. Automated detection of dental restorations using deep learning on panoramic radiographs. Dentomaxillofacial Radiol. 2022. https://doi.org/10.1259/dmfr.20220244.

Article  Google Scholar 

Celik ME. Deep learning based detection tool for impacted mandibular third molar teeth. Diagnostics. 2022;12(4):942.

Article  PubMed  PubMed Central  Google Scholar 

Chang HJ, et al. Deep learning hybrid method to automatically diagnose periodontal bone loss and stage periodontitis. Sci Rep. 2020. https://doi.org/10.1038/s41598-020-64509-z.

Article  PubMed  PubMed Central  Google Scholar 

Çelik B, Savaştaer EF, Kaya HI, Çelik ME. The role of deep learning for periapical lesion detection on panoramic radiographs. Dentomaxillofacial Radiol. 2023;52(8):20230118.

Article  Google Scholar 

Kwon O, et al. Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network. Dentomaxillofacial Radiol. 2020;49(8):20200185.

Article  Google Scholar 

Chen H, et al. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Sci Rep. 2019. https://doi.org/10.1038/s41598-019-40414-y.

Article  PubMed  PubMed Central  Google Scholar 

Çelik B, Çelik ME. Root dilaceration using deep learning: a diagnostic approach. Appl Sci. 2023;13(14):8260.

Article  Google Scholar 

Xiao TT, et al. Unified perceptual parsing for scene understanding. In: 15th European conference on computer vision, 2018. Munich, Germany: Springer. https://doi.org/10.1007/978-3-030-01267-0

Chapter  Google Scholar 

Zhao HS, et al. Pyramid scene parsing network. 30th Ieee conference on computer vision and pattern recognition (Cvpr 2017). 2017; pp. 6230–6239

Wang XL, et al. Non-local neural networks. 2018 Ieee/Cvf conference on computer vision and pattern recognition (Cvpr). 2018; pp. 7794-7803

Li X, et al. Expectation-maximization attention networks for semantic segmentation. 2019 Ieee/Cvf international conference on computer vision (Iccv 2019). 2019; pp. 9166–9175

He JJ, Deng ZY, Qiao Y. Dynamic multi-scale filters for semantic segmentation. 2019 Ieee/Cvf international conference on computer vision (Iccv 2019). 2019; pp. 3561–3571

Ye M, et al. Deep learning for person re-identification: a survey and outlook. IEEE Trans Pattern Anal Mach Intell. 2022;44(6):2872–93.

Article  PubMed  Google Scholar 

Goodfellow I, Bengio Y, Courville A. Deep learning. In: Adaptive Computation and Machine Learning Series, vol. xxii. Cambridge: MIT Press; 2016. p. 775.

Google Scholar 

Buckley M, Spangberg LSW. The prevalence and technical quality of endodontic treatment in an American subpopulation. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 1995;79(1):92–100.

Article  CAS  PubMed  Google Scholar 

Okano T, Sur J. Radiation dose and protection in dentistry. Jpn Dent Sci Rev. 2010;46(2):112–21.

Article  Google Scholar 

Buyuk C, Alpay BA, Er F. Detection of the separated root canal instrument on panoramic radiograph: a comparison of LSTM and CNN deep learning methods. Dentomaxillofacial Radiol. 2023. https://doi.org/10.1259/dmfr.20220209.

Article  Google Scholar 

Choi HR, et al. Automatic detection of teeth and dental treatment patterns on dental panoramic radiographs using deep neural networks. Forensic Sci Res. 2022;7(3):456–66.

Article  PubMed  PubMed Central  Google Scholar 

Sherwood AA, et al. A deep learning approach to segment and classify C-shaped canal morphologies in mandibular second molars using cone-beam computed tomography. J Endod. 2021;47(12):1907–16.

Article  PubMed  Google Scholar 

Zhang LJ, et al. A lightweight convolutional neural network model with receptive field block for C-shaped root canal detection in mandibular second molars. Sci Rep. 2022. https://doi.org/10.1038/s41598-022-20411-4.

Article  PubMed  PubMed Central  Google Scholar 

Jeon SJ, et al. Deep-learning for predicting C-shaped canals in mandibular second molars on panoramic radiographs. Dentomaxillofacial Radiol. 2021;50(5):20200513.

Article  Google Scholar 

Ari T, et al. Automatic feature segmentation in dental periapical radiographs. Diagnostics. 2022;12(12):3081.

Article  PubMed  PubMed Central  Google Scholar 

Wang YW, et al. Root canal treatment planning by automatic tooth and root canal segmentation in dental CBCT with deep multi-task feature learning. Med Image Anal. 2023;85:102750.

Article  PubMed  Google Scholar 

Duan W, et al. Refined tooth and pulp segmentation using U-Net in CBCT image. Dentomaxillofacial Radiol. 2021;50(6):20200251.

Article  Google Scholar 

Comments (0)

No login
gif