Deep Learning in Dentistry: A Literature Review

키워드

참고문헌 (131)

1. Schwendicke F, "Artificial intelligence in dentistry : chances and challenges", J Dent Res, 99, pp.769~774 (2020)
2. Goodfellow I, "Deep learning" (2016), MIT press
3. Hung K, "The use and performance of artificial intelligence applications in dental and maxillofacial radiology : a systematic review", Dentomaxillofac Radiol, 49, pp.20190107~ (2020)
4. Rodrigues JA, "Demystifying artificial intelligence and deep learning in dentistry", Braz Oral Res, 35, pp.e094~ (2021)
5. 황재준, "An overview of deep learning in the field of dentistry", Imaging Science in Dentistry, 49(1), pp.1~7 (2019), 대한영상치의학회[DOI]
6. Corbella S, "Applications of deep learning in dentistry", Oral Surg Oral Med Oral Pathol Oral Radiol, 132, pp.225~238 (2021)
7. Carrillo-Perez F, "Applications of artificial intelligence in dentistry : a comprehensive review", J Esthet Restor Dent, 34, pp.259~280 (2022)
8. Schwendicke F, "Deep learning for cephalometric landmark detection : systematic review and meta-analysis", Clin Oral Investig, 25, pp.4299~4309 (2021)
9. Hutton B, "The PRISMA statement extension for systematic reviews incorporating network metaanalysis : PRISMA-NMA", Med Clin(Barc), 147, pp.262~266 (2016)
10. Schardt C, "Utilization of the PICO framework to improve searching PubMed for clinical questions", BMC Med Inform Decis Mak, 7, pp.16~ (2007)
11. Miki Y, "Classification of teeth in cone-beam CT using deep convolutional neural network", Comput Biol Med, 80, pp.24~29 (2017)
12. Zhang K, "An effective teeth recognition method using label tree with cascade network structure", Comput Med Imaging Graph, 68, pp.61~70 (2018)
13. Chen H, "A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films", Sci Rep, 9, pp.3840~ (2019)
14. Hiraiwa T, "A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography", Dentomaxillofac Radiol, 48, pp.20180218~ (2019)
15. Tuzoff DV, "Tooth detection and numbering in panoramic radiographs using convolutional neural networks", Dentomaxillofac Radiol, 48, pp.20180051~ (2019)
16. Vinayahalingam S, "Automated detection of third molars and mandibular nerve by deep learning", Sci Rep, 9, pp.9007~ (2019)
17. Xu X, "3D Tooth segmentation and labeling using deep convolutional neural networks", IEEE Trans Vis Comput Graph, 25, pp.2336~2348 (2019)
18. Jaskari J, "Deep learning method for mandibular canal segmentation in dental cone beam computed tomography volumes", Sci Rep, 10, pp.5842~ (2020)
19. Kwak GH, "Automatic mandibular canal detection using a deep convolutional neural network", Sci Rep, 10, pp.5711~ (2020)
20. Lee JH, "Application of a fully deep convolutional neural network to the automation of tooth segmentation on panoramic radiographs", Oral Surg Oral Med Oral Pathol Oral Radiol, 129, pp.635~642 (2020)
21. Mahdi FP, "Optimization technique combined with deep learning method for teeth recognition in dental panoramic radiographs", Sci Rep, 10, pp.19261~ (2020)
22. Bilgir E, "An artifıcial ıntelligence approach to automatic tooth detection and numbering in panoramic radiographs", BMC Med Imaging, 21, pp.124~ (2021)
23. Cha JY, "Panoptic segmentation on panoramic radiographs : deep learning-based segmentation of various structures including maxillary sinus and mandibular canal", J Clin Med (2021)
24. Jeon SJ, "Deep-learning for predicting C-shaped canals in mandibular second molars on panoramic radiographs", Dentomaxillofac Radiol, 50, pp.20200513~ (2021)
25. Kılıc MC, "Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs", Dentomaxillofac Radiol, 50, pp.20200172~ (2021)
26. Lahoud P, "Artificial intelligence for fast and accurate 3-dimensional tooth segmentation on cone-beam computed tomography", J Endod, 47, pp.827~835 (2021)
27. Leite AF, "Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs", Clin Oral Investig, 25, pp.2257~2267 (2021)
28. Lin X, "Microcomputed tomography-guided artificial intelligence for pulp cavity and tooth segmentation on cone-beam computed tomography", J Endod, 47, pp.1933~1941 (2021)
29. Muramatsu C, "Tooth detection and classification on panoramic radiographs for automatic dental chart filing : improved classification by multi-sized input data", Oral Radiol, 37, pp.13~19 (2021)
30. Prados-Privado M, "A validation employing convolutional neural network for the radiographic detection of absence or presence of teeth", J Clin Med (2021)
31. Prados-Privado M, "A convolutional neural network for automatic tooth numbering in panoramic images", Biomed Res Int, 2021, pp.3625386~ (2021)
32. Shaheen E, "A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. A validation study", J Dent, 115, pp.103865~ (2021)
33. Sherwood AA, "A deep learning approach to segment and classify C-shaped canal morphologies in mandibular second molars using cone-beam computed tomography", J Endod, 47, pp.1907~1916 (2021)
34. Verhelst PJ, "Layered deep learning for automatic mandibular segmentation in cone-beam computed tomography", J Dent, 114, pp.103786~ (2021)
35. Yasa Y, "An artificial intelligence proposal to automatic teeth detection and numbering in dental bite-wing radiographs", Acta Odontol Scand, 79, pp.275~281 (2021)
36. Yüksel AE, "Dental enumeration and multiple treatment detection on panoramic X-rays using deep learning", Sci Rep, 11, pp.12342~ (2021)
37. Gupta A, "A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images", Int J Comput Assist Radiol Surg, 10, pp.1737~1752 (2015)
38. Jung S-K, "New approach for the diagnosis of extractions with neural network machine learning", Am J Orthod Dentofacial Orthop, 149, pp.127~133 (2016)
39. Arik SÖ, "Fully automated quantitative cephalometry using convolutional neural networks", J Med Imaging, 4, pp.014501~ (2017)
40. Codari M, "Computer-aided cephalometric landmark annotation for CBCT data", Int J Comput Assist Radiol Surg, 12, pp.113~121 (2017)
41. Montúfar J, "Automatic 3-dimensional cephalometric landmarking based on active shape models in related projections", Am J Orthod Dentofacial Orthop, 153, pp.449~458 (2018)
42. Park J-H, "Automated identification of cephalometric landmarks : Part 1-Comparisons between the latest deep-learning methods YOLOV3and SSD", Angle Orthod, 89, pp.903~909 (2019)
43. Lee JH, "Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks", BMC Oral Health, 20, pp.270~ (2020)
44. Kim J, "Accuracy of automated identification of lateral cephalometric landmarks using cascade convolutional neural networks on lateral cephalograms from nationwide multi-centres", Orthod Craniofac Res 2021;24 Suppl, 2, pp.59~67
45. Kim M, "Realistic high-resolution lateral cephalometric radiography generated by progressive growing generative adversarial network and quality evaluations", Sci Rep, 11, pp.12563~ (2021)
46. 김영현, "A fully deep learning model for the automatic identification of cephalometric landmarks", Imaging Science in Dentistry, 51(3), pp.299~306 (2021), 대한영상치의학회[DOI]
47. Kim YH, "Influence of the depth of the convolutional neural networks on an artificial intelligence model for diagnosis of orthognathic surgery", J Pers Med (2021)
48. Shin W, "Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals", BMC Oral Health, 21, pp.130~ (2021)
49. Kavitha MS, "Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches", Dentomaxillofac Radiol, 45, pp.20160076~ (2016)
50. Yilmaz E, "Computer-aided diagnosis of periapical cyst and keratocystic odontogenic tumor on cone beam computed tomography", Comput Methods Programs Biomed, 146, pp.91~100 (2017)
51. Kim DW, "Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction : a preliminary report", Bone, 116, pp.207~214 (2018)
52. Uthoff RD, "Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities", PLoS One, 13, pp.e0207493~ (2018)
53. Ariji Y, "Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence", Oral Surg Oral Med Oral Pathol Oral Radiol, 127, pp.458~463 (2019)
54. Ariji Y, "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, 128, pp.424~430 (2019)
55. Kats L, "Atherosclerotic carotid plaque on panoramic radiographs : neural network detection", Int J Comput Dent, 22, pp.163~169 (2019)
56. Kim DW, "Deep learning-based survival prediction of oral cancer patients", Sci Rep, 9, pp.6994~ (2019)
57. Kim HG, "Improvement diagnostic accuracy of sinusitis recognition in paranasal sinus X-ray using multiple deep learning models", Quant Imaging Med Surg, 9, pp.942~951 (2019)
58. Lee JS, "Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system : a preliminary study", Dentomaxillofac Radiol, 48, pp.20170344~ (2019)
59. Murata M, "Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography", Oral Radiol, 35, pp.301~307 (2019)
60. Yu M, "Deep convolutional neural networks for tongue squamous cell carcinoma classification using Raman spectroscopy", Photodiagnosis Photodyn Ther, 26, pp.430~435 (2019)
61. Endres MG, "Development of a deep learning algorithm for periapical disease detection in dental radiographs", Diagnostics (Basel), 10 (2020)
62. Kwon O, "Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network", Dentomaxillofac Radiol, 49, pp.20200185~ (2020)
63. Lee JH, "Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network", Oral Dis, 26, pp.152~158 (2020)
64. Lee KS, "Evaluation of transfer learning with deep convolutional neural networks for screening osteoporosis in dental panoramic radiographs", J Clin Med, 9, pp.392~ (2020)
65. Yang H, "Deep learning for automated detection of cyst and tumors of the jaw in panoramic radiographs", J Clin Med, 9, pp.1839~ (2020)
66. Ahn Y, "Automated mesiodens classification system using deep learning on panoramic radiographs of children", Diagnostics(Basel), 11, pp.1477~ (2021)
67. Alhazmi A, "Application of artificial intelligence and machine learning for prediction of oral cancer risk", J Oral Pathol Med, 50, pp.444~450 (2021)
68. Camalan S, "Convolutional neural network-based clinical predictors of oral dysplasia : class activation map analysis of deep learning results", Cancers(Basel), 13, pp.1291~ (2021)
69. Jung SK, "Deep active learning for automatic segmentation of maxillary sinus lesions using a convolutional neural network", Diagnostics(Basel), 11, pp.688~ (2021)
70. Kim BS, "Deep learning-based prediction of paresthesia after third molar extraction : a preliminary study", Diagnostics(Basel), 11, pp.1572~ (2021)
71. Kuwada C, "Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system", Sci Rep, 11, pp.16044~ (2021)
72. Mori M, "A deep transfer learning approach for the detection and diagnosis of maxillary sinusitis on panoramic radiographs", Odontology, 109, pp.941~948 (2021)
73. Musulin J, "An enhanced histopathology analysis : an AI-based system for multiclass grading of oral squamous cell carcinoma and segmenting of epithelial and stromal tissue", Cancers(Basel), 13, pp.1784~ (2021)
74. Pratama R, "Authentication of differential gene expression in oral squamous cell carcinoma using machine learning applications", BMC Oral Health, 21, pp.281~ (2021)
75. Rao RS, "Deep learning-based microscopic diagnosis of odontogenic keratocysts and non-keratocysts in haematoxylin and eosin-stained incisional biopsies", Diagnostics(Basel), 11, pp.2184~ (2021)
76. Song B, "Classification of imbalanced oral cancer image data from high-risk population", J Biomed Opt, 26, pp.105001~ (2021)
77. Yoo JH, "Deep learning based prediction of extraction difficulty for mandibular third molars", Sci Rep, 11, pp.1954~ (2021)
78. Johari M, "Detection of vertical root fractures in intact and endodontically treated premolar teeth by designing a probabilistic neural network : an ex vivo study", Dentomaxillofac Radiol, 46, pp.20160107~ (2017)
79. Lee JH, "Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm", J Dent, 77, pp.106~111 (2018)
80. Zanella-Calzada LA, "Deep artificial neural networks for the diagnostic of caries using socioeconomic and nutritional features as determinants : data from NHANES 2013-2014", Bioengineering(Basel), 5, pp.47~ (2018)
81. Casalegno F, "Caries detection with near-infrared transillumination using deep learning", J Dent Res, 98, pp.1227~1233 (2019)
82. Ekert T, "Deep learning for the radiographic detection of apical lesions", J Endod, 45, pp.917~922 (2019)[DOI]
83. Cantu AG, "Detecting caries lesions of different radiographic extension on bitewings using deep learning", J Dent, 100, pp.103425~ (2020)
84. Schwendicke F, "Deep learning for caries lesion detection in near-infrared light transillumination images : a pilot study", J Dent, 92, pp.103260~ (2020)
85. Setzer FC, "Artificial intelligence for the computer-aided detection of periapical lesions in cone-beam computed tomographic images", J Endod, 46, pp.987~993 (2020)
86. Askar H, "Detecting white spot lesions on dental photography using deep learning : a pilot study", J Dent, 107, pp.103615~ (2021)
87. Bayrakdar IS, "Deep-learning approach for caries detection and segmentation on dental bitewing radiographs", Oral Radiol, 38, pp.468~479 (2022)[DOI]
88. Holtkamp A, "Generalizability of deep learning models for caries detection in near-infrared light transillumination images", J Clin Med, 10, pp.961~ (2021)
89. Hur SH, "Machine learning to predict distal caries in mandibular second molars associated with impacted third molars", Sci Rep, 11, pp.15447~ (2021)
90. Krois J, "Generalizability of deep learning models for dental image analysis", Sci Rep, 11, pp.6102~ (2021)
91. Lee S, "Deep learning for early dental caries detection in bitewing radiographs", Sci Rep, 11, pp.16807~ (2021)
92. Lian L, "Deep learning for caries detection and classification", Diagnostics(Basel), 11, pp.1672~ (2021)
93. Moran M, "Classification of approximal caries in bitewing radiographs using convolutional neural networks", Sensors(Basel), 21, pp.5192~ (2021)
94. Vinayahalingam S, "Classification of caries in third molars on panoramic radiographs using deep learning", Sci Rep, 11, pp.12609~ (2021)
95. Jae-Hong Lee, "Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm", Journal of Periodontal & Implant Science, 48(2), pp.114~123 (2018), 대한치주과학회
96. Kim J, "DeNTNet : Deep neural transfer network for the detection of periodontal bone loss using panoramic dental radiographs", Sci Rep, 9, pp.17615~ (2019)
97. Krois J, "Deep learning for the radiographic detection of periodontal bone loss", Sci Rep, 9, pp.8495~ (2019)
98. Chang HJ, "Deep learning hybrid method to automatically diagnose periodontal bone loss and stage periodontitis", Sci Rep, 10, pp.7531~ (2020)
99. Li W, "A deep learning approach to automatic gingivitis screening based on classification and localization in RGB photos", Sci Rep, 11, pp.16831~ (2021)
100. Hadj Saïd M, "Development of an artificial intelligence model to identify a dental implant from a radiograph", Int J Oral Maxillofac Implants, 36, pp.1077~1082 (2020)
101. Kim JE, "Transfer learning via deep neural networks for implant fixture system classification using periapical radiographs", J Clin Med, 9, pp.1117~ (2020)
102. Lee JH, "Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs : a pilot study", Medicine(Baltimore), 99, pp.e20787~ (2020)
103. Sukegawa S, "Deep neural networks for dental implant system classification", Biomolecules, 10, pp.984~ (2020)
104. Cha JY, "Peri-implant bone loss measurement using a region-based convolutional neural network on dental periapical radiographs", J Clin Med, 10, pp.1009~ (2021)
105. Lee DW, "Artificial intelligence in fractured dental implant detection and classification : evaluation using dataset from two dental hospitals", Diagnostics(Basel), 11, pp.233~ (2021)
106. Sukegawa S, "Multi-task deep learning model for classification of dental implant brand and treatment stage using dental panoramic radiograph images", Biomolecules, 11, pp.815~ (2021)
107. Wei J, "Evaluation of a novel computer color matching system based on the improved back-propagation neural network model", J Prosthodont, 27, pp.775~783 (2018)
108. Yamaguchi S, "Predicting the debonding of CAD", CAM composite resin crowns with AI, 2019, pp.98, 1234~1238
109. Takahashi T, "Deep learning-based detection of dental prostheses and restorations", Sci Rep, 11, pp.1960~ (2021)
110. De Tobel J, "An automated technique to stage lower third molar development on panoramic radiographs for age estimation : a pilot study", J Forensic Odontostomatol, 35, pp.42~54 (2017)
111. Banar N, "Towards fully automated third molar development staging in panoramic radiographs", Int J Legal Med, 134, pp.1831~1841 (2020)
112. Fan F, "Automatic human identification from panoramic dental radiographs using the convolutional neural network", Forensic Sci Int, 314, pp.110416~ (2020)
113. Matsuda S, "Personal identification with orthopantomography using simple convolutional neural networks : a preliminary study", Sci Rep, 10, pp.13559~ (2020)
114. Merdietio Boedi R, "Effect of lower third molar segmentations on automated tooth development staging using a convolutional neural network", J Forensic Sci, 65, pp.481~486 (2020)
115. Guo YC, "Accurate age classification using manual method and deep convolutional neural network based on orthopantomogram images", Int J Legal Med, 135, pp.1589~1597 (2021)
116. Kim S, "Age-group determination of living individuals using first molar images based on artificial intelligence", Sci Rep, 11, pp.1073~ (2021)
117. Zheng Q, "Age estimation based on 3D pulp chamber segmentation of first molars from cone-beam-computed tomography by integrated deep learning and level set", Int J Legal Med, 135, pp.365~373 (2021)
118. Shoukri B, "Minimally invasive approach for diagnosing TMJ osteoarthritis", J Dent Res, 98, pp.1103~1111 (2019)
119. You W, "Deep learning-based dental plaque detection on primary teeth : a comparison with clinical assessments", BMC Oral Health, 20, pp.141~ (2020)
120. Cejudo JE, "Classification of dental radiographs using deep learning", J Clin Med, 10, pp.1496~ (2021)
121. Choi E, "Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram", Sci Rep, 11, pp.10246~ (2021)
122. Khan HA, "Automated feature detection in dental periapical radiographs by using deep learning", Oral Surg Oral Med Oral Pathol Oral Radiol, 131, pp.711~720 (2021)
123. Kokomoto K, "Intraoral image generation by progressive growing of generative adversarial network and evaluation of generated image quality by dentists", Sci Rep, 11, pp.18517~ (2021)
124. Pauwels R, "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, pp.610~616 (2021)
125. You WZ, "Deep learning-based dental plaque detection on permanent teeth and the influenced factors", Zhonghua Kou Qiang Yi Xue Za Zhi, 56, pp.665~671 (2021)
126. Cheng B, "Revisiting RCNN: on awakening the classification power of faster RCNN", Proceedings of the European Conference on Computer Vision (ECCV), pp.453~468 (2018)
127. Ren S, "Faster R-CNN : Towards realtime object detection with region proposal networks", Adv Neural Inf Process Syst (2015)
128. Du J, "Understanding of object detection based on CNN family and YOLO", J Phys : Conf Ser, 1004, pp.012029~ (2018)
129. Frid-Adar M, "Synthetic data augmentation using GAN for improved liver lesion classification", IEEE International Symposium on Biomedical Imaging(ISBI, pp.289~293 (2018)
130. Sandfort V, "Data augmentation using generative adversarial networks(CycleGAN)to improve generalizability in CT segmentation tasks", Sci Rep, 9, pp.1~9 (2019)
131. Rajpurkar P, "AI in health and medicine", Nat Med, 28, pp.31~38 (2022)

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