Endodontide Yapay Zeka

Öztürk K, Şahin ME. Yapay sinir ağları ve yapay zekâ’ya genel bir bakış. Takvim-i Vekayi. 2018;6(2):25-36.

Aminoshariae A, Kulild J, Nagendrababu V. Artificial Intelligence in Endodontics: Current Applications and Future Directions. J Endod. 2021;47(9):1352-7. https://doi.org/10.1016/j.joen.2021.06.003

Lee J-H, Kim D-H, Jeong S-N et al. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Journal of dentistry. 2018;77:106-11. https://doi.org/10.1016/j.jdent.2018.07.015

Hwang J-J, Jung Y-H, Cho B-H,et al. An overview of deep learning in the field of dentistry. Imaging science in dentistry. 2019;49(1):1-7. https://doi.org/10.5624/isd.2019.49.1.1

Jha S, Topol EJ. Adapting to artificial intelligence: radiologists and pathologists as information specialists. Jama. 2016;316(22):2353-4. 10.1001/jama.2016.17438

Chan S, Siegel EL. Will machine learning end the viability of radiology as a thriving medical specialty? The British journal of radiology.2019;92(1094):20180416.https://doi.org/10.1259/bjr.20180416

Katne T, Kanaparthi A, Gotoor S, et al. Artificial intelligence: demystifying dentistry–the future and beyond. Int J Contemp Med Surg Radiol. 2019;4(4):D6-D9. http://dx.doi.org/10.21276/ijcmsr.2019.4.4.2

Tuzoff DV, Tuzova LN, Bornstein MM, et al. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofacial Radiology. 2019;48(4):20180051. https://doi.org/10.1259/dmfr.20180051

Davies A, Mannocci F, Mitchell P, et al. The detection of periapical pathoses in root filled teeth using single and parallax periapical radiographs versus cone beam computed tomography–a clinical study. International Endodontic Journal. 2015;48(6):582-92. https://doi.org/10.1111/iej.12352

Krois J, Ekert T, Meinhold L, et al. Deep learning for the radiographic detection of periodontal bone loss. Scientific reports. 2019;9(1):1-6.

Poedjiastoeti W, Suebnukarn S. Application of convolutional neural network in the diagnosis of jaw tumors. Healthcare informatics research. 2018;24(3):236-41. https://doi.org/10.4258/hir.2018.24.3.236

Kim DW, Lee S, Kwon S, et al. Deep learning-based survival prediction of oral cancer patients. Scientific reports. 2019;9(1):1-10. https://doi.org/10.1038/s41598-019-43372-7

Lee J-H, Lee J-S, Choi J-K, et al. National dental policies and socio-demographic factors affecting changes in the incidence of periodontal treatments in Korean: a nationwide population-based retrospective cohort study from 2002–2013. BMC Oral Health. 2016;16(1):1-9.

Mohammad‐Rahimi H, Motamedian SR, Pirayesh Z, et al. Deep learning in periodontology and oral implantology: A scoping review. Journal of Periodontal Research 57.5 (2022): 942-951. https://doi.org/10.1111/jre.13037

Lee D-W, Kim S-Y, Jeong S-N, et al. Artificial intelligence in fractured dental implant detection and classification: evaluation using dataset from two dental hospitals. Diagnostics. 2021;11(2):233. https://doi.org/10.3390/diagnostics11020233

Kurt Bayrakdar S, Orhan K, Bayrakdar IS, et al. A deep learning approach for dental implant planning in cone-beam computed tomography images. BMC Medical Imaging. 2021;21(1):1-9.

Murata S, Lee C, Tanikawa C, Date S, editors. Towards a fully automated diagnostic system for orthodontic treatment in dentistry. 2017 IEEE 13th international conference on e-science (e-science); 2017: IEEE.

Leonardi R, Giordano D, Maiorana F, et al. Automatic cephalometric analysis: a systematic review. The Angle Orthodontist. 2008;78(1):145-51. https://doi.org/10.2319/120506-491.1

Gupta A, Kharbanda OP, Sardana V, et al. A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images. International journal of computer assisted radiology and surgery. 2015;10(11):1737-52. DOI 10.1007/s11548-015-1173-6

Montúfar J, Romero M, Scougall-Vilchis RJ. Automatic 3-dimensional cephalometric landmarking based on active shape models in related projections. American Journal of Orthodontics and Dentofacial Orthopedics.2018;153(3):449.58https://doi.org/10.1016/j.ajodo.2017.06.028

Montúfar J, Romero M, Scougall-Vilchis RJ. Hybrid approach for automatic cephalometric landmark annotation on cone-beam computed tomography volumes. American Journal of Orthodontics and Dentofacial Orthopedics. 2018;154(1):140-50. https://doi.org/10.1016/j.ajodo.2017.08.028

Jung S-K, Kim T-W. New approach for the diagnosis of extractions with neural network machine learning. American Journal of Orthodontics and Dentofacial Orthopedics. 2016;149(1):127-33. https://doi.org/10.1016/j.ajodo.2015.07.030

Patcas R, Bernini DA, Volokitin A, et al. Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age. International journal of oral and maxillofacial surgery. 2019;48(1):77-83. https://doi.org/10.1016/j.ijom.2018.07.010

Le Y, Verdonschot E. Performance of diagnostic systems in occlusal caries detection compared. Community Dentistry and Oral Epidemiology. 1994;22(3):187-91. https://doi.org/10.1111/j.1600-0528.1994.tb01838.x

Akpata E, Farid M, Al-Saif K, et al. Cavitation at radiolucent areas on proximal surfaces of posterior teeth. Caries research. 1996;30(5):313-6. https://doi.org/10.1159/000262336

Srivastava MM, Kumar P, Pradhan L, et al. Detection of tooth caries in bitewing radiographs using deep learning. arXiv preprint arXiv:171107312. 2017. https://doi.org/10.48550/arXiv.1711.07312

Cantu AG, Gehrung S, Krois J, et al. Detecting caries lesions of different radiographic extension on bitewings using deep learning. Journal of dentistry. 2020;100:103425. https://doi.org/10.1016/j.jdent.2020.103425

Askar H, Krois J, Rohrer C, et al. Detecting white spot lesions on dental photography using deep learning: A pilot study. Journal of dentistry. 2021;107:103615. https://doi.org/10.1016/j.jdent.2021.103615

Casalegno F, Newton T, Daher R, et al. Caries detection with near-infrared transillumination using deep learning. Journal of dental research. 2019;98(11):1227-33.

Khanagar SB, Al-Ehaideb A, Maganur PC, et al. Developments, application, and performance of artificial intelligence in dentistry–A systematic review. Journal of dental sciences. 2021;16(1):508-22. https://doi.org/10.1016/j.jds.2020.06.019

De Tobel J, Radesh P, Vandermeulen D, et al. An automated technique to stage lower third molar development on panoramic radiographs for age estimation: a pilot study. The Journal of forensic odonto-stomatology. 2017;35(2):42.

Patil V, Vineetha R, Vatsa S, et al. Artificial neural network for gender determination using mandibular morphometric parameters: A comparative retrospective study. Cogent Engineering. 2020;7(1):1723783. https://doi.org/10.1080/23311916.2020.1723783

Widmann G. Image-guided surgery and medical robotics in the cranial area. Biomedical imaging and intervention journal. 2007;3(1). doi: 10.2349/biij.3.1.e11

Pereira KR, Sinha R. Welcome the “new kid on the block” into the family: artificial intelligence in oral and maxillofacial surgery. British Journal of Oral and Maxillofacial Surgery. 2020;58(1):83-4.

Zhang W, Li J, Li Z-B, et al. Predicting postoperative facial swelling following impacted mandibular third molars extraction by using artificial neural networks evaluation. Scientific reports. 2018;8(1):1-9. DOI:10.1038/s41598-018-29934-1

Yoo J-H, Yeom H-G, Shin W, et al. Deep learning based prediction of extraction difficulty for mandibular third molars. Scientific Reports. 2021;11(1):1-9. https://doi.org/10.1016/j.jdent.2023.104430

Lim K, Moles D, Downer M, et al. Opportunistic screening for oral cancer and precancer in general dental practice: results of a demonstration study. British dental journal. 2003;194(9):497-502.

Ilhan B, Lin K, Guneri P, et al. Improving oral cancer outcomes with imaging and artificial intelligence. Journal of dental research. 2020;99(3):241-8. https://doi.org/10.1177/0022034520902128

Vera V, Corchado E, Redondo R, et al. Applying soft computing techniques to optimise a dental milling process. Neurocomputing. 2013;109:94-104. https://doi.org/10.1016/j.neucom.2012.04.033

Khanna SS, Dhaimade PA. Artificial intelligence: transforming dentistry today. Indian J Basic Appl Med Res. 2017;6(3):161-7.

Wang Y, Hays R, Marcus M, et al. Developing Children’s Oral Health Assessment Toolkits Using Machine Learning Algorithm. JDR Clinical & Translational Research. 2020;5(3):233-43. https://doi.org/10.1177/2380084419885612

Cote CD, Kim PJ. Artificial intelligence in anesthesiology: Moving into the future. University of Toronto Medical Journal. 2019;96(1).

Ekert T, Krois J, Meinhold L, et al. Deep learning for the radiographic detection of apical lesions. Journal of endodontics. 2019;45(7):917-22. e5. https://doi.org/10.1016/j.joen.2019.03.016

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

Endres MG, Hillen F, Salloumis M, et al. Development of a deep learning algorithm for periapical disease detection in dental radiographs. Diagnostics.2020;10(6):430.https://doi.org/10.3390/diagnostics10060430

Bayrakdar IS, Orhan K, Çelik Ö, et al. A U-Net Approach to Apical Lesion Segmentation on Panoramic Radiographs. BioMed ResearchInternational. 2022;2022. https://doi.org/10.1155/2022/7035367

Orhan K, Bayrakdar IS, Ezhov M, et al. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int Endod J. 2020;53(5):680-9. https://doi.org/10.1111/iej.13265

Talwar S, Utneja S, Nawal RR, et al. Role of cone-beam computed tomography in diagnosis of vertical root fractures: a systematic review and meta-analysis. Journal of Endodontics. 2016;42(1):12-24. https://doi.org/10.1016/j.joen.2015.09.012

Johari M, Esmaeili F, Andalib A, et al. Detection of vertical root fractures in intact and endodontically treated premolar teeth by designing a probabilistic neural network: an ex vivo study. Dentomaxillofacial Radiology. 2017;46(2):20160107. https://doi.org/10.1259/dmfr.20160107

Fukuda M, Inamoto K, Shibata N, et al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiol. 2020;36(4):337-43.

Saghiri MA, Asgar K, Boukani K, et al. A new approach for locating the minor apical foramen using an artificial neural network. International endodontic journal. 2012;45(3):257-65. https://doi.org/10.1111/j.1365-2591.2011.01970.x

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

Bruellmann D, Tjaden H, Schwanecke U, et al. An optimized video system for augmented reality in endodontics: a feasibility study. Clinical oral investigations. 2013;17(2):441-8.

Sherwood AA, Sherwood AI, Setzer FC, et al. A Deep Learning Approach to Segment and Classify C-Shaped Canal Morphologies in Mandibular Second Molars Using Cone-beam Computed Tomography. Journal of Endodontics. 2021;47(12):1907-16. https://doi.org/10.1016/j.joen.2021.09.009

Qu Y, Lin Z, Yang Z, et al. Machine learning models for prognosis prediction in endodontic microsurgery. Journal of Dentistry. 2022;118:103947. https://doi.org/10.1016/j.jdent.2022.103947

Gao X, Xin X, Li Z, et al. Predicting postoperative pain following root canal treatment by using artificial neural network evaluation. Scientific reports. 2021;11(1):1-8.

Li Y, Zeng G, Zhang Y, et al. Agmb-transformer: Anatomy-guided multi-branch transformer network for automated evaluation of root canal therapy. IEEE Journal of Biomedical and Health Informatics. 2021;26(4):1684-95. DOI: 10.1109/JBHI.2021.3129245

Campo L, Aliaga IJ, De Paz JF, et al. Retreatment Predictions in Odontology by means of CBR Systems. Computational Intelligence and Neuroscience. 2016;2016. https://doi.org/10.1155/2016/7485250

Li S, Liu J, Zhou Z, et al. Artificial intelligence for caries and periapical periodontitis detection. Journal of Dentistry. 2022;122:104107. https://doi.org/10.1016/j.jdent.2022.104107