All Issue

2020 Vol.24, Issue 3 Preview Page

Review Article


September 2020. pp. 148-181
Abstract


References
1 
Hype cycle. Gartner. https://www.gartner.com/smarterwithgartner/top-trends-on-the-gartner-hypecycle-for-artificial-intelligence-2019/ (2019).
2 
Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems. 2012. p.1097-105.
3 
Fukushima K. Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 1980;36:193-202.
10.1007/BF003442517370364
4 
Hubel DH, Wiesel TN. Receptive fields of single neurones in the cat's striate cortex. J Physiol 1959;148:574-91.
10.1113/jphysiol.1959.sp00630814403679PMC1363130
5 
LeCun Y, Bottou L, Bengio Y, Haffner, P. Gradient-based learning applied to document recognition. Proc IEEE 1998;86:2278-323.
10.1109/5.726791
6 
Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer; 2015. p.234-41.
10.1007/978-3-319-24574-4_28
7 
Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 2017;39:1137-49.
10.1109/TPAMI.2016.257703127295650
8 
Suwajanakorn S, Seitz SM, Kemelmacher-Shlizerman I. Synthesizing Obama: learning lip sync from audio. In: ACM Transactions on Graphics 2017;36:1-13.
10.1145/3072959.3073640
9 
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997;9:1735-80.
10.1162/neco.1997.9.8.17359377276
10 
Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference. 2014. p.1724-34.
10.3115/v1/D14-1179
11 
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. In: Advances in Neural Information Processing Systems. 2014. p.2672-80.
12 
Chen H, Zhang K, Lyu P, Li H, Zhang L, Wu J, et al. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Sci Rep 2019;9:1-11.
10.1038/s41598-019-40414-y30846758PMC6405755
13 
Tuzoff DV, Tuzova LN, Bornstein MM, Krasnov AS, Kharchenko MA, Nikolenko SI, et al. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofac Radiol 2019;48:20180051.
10.1259/dmfr.2018005130835551PMC6592580
14 
Zhang K, Wu J, Chen H, Lyub P. An effective teeth recognition method using label tree with cascade network structure. Comput Med Imaging Graph 2018;68:61-70.
10.1016/j.compmedimag.2018.07.00130056291
15 
Jader G, Fontineli J, Ruiz M, Abdalla K, Pithon M, Oliveira L. Deep instance segmentation of teeth in panoramic X-ray images. In: 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). 2018;400-7.
10.1109/SIBGRAPI.2018.00058
16 
Merdietio Boedi R, Banar N, De Tobel J, Bertels J, Vandermeulen D, Thevissen PW. Effect of lower third molar segmentations on automated tooth development staging using a convolutional neural network. J Forensic Sci 2020;65:481-6.
10.1111/1556-4029.1418231487052
17 
Tian S, Dai N, Zhang B, Yuan F, Yu Q, Cheng X. Automatic classification and segmentation of teeth on 3D dental model using hierarchical deep learning networks. IEEE Access 2019;7:84817-28.
10.1109/ACCESS.2019.2924262
18 
Xu X, Liu C, Zheng Y. 3D tooth segmentation and labeling using deep convolutional neural networks. IEEE Trans Vis Comput Graph 2019;25:2336-48.
10.1109/TVCG.2018.283968529994311
19 
Minnema J, Eijnatten M, Hendriksen AA, Liberton N, Pelt DM, Batenburg KJ, et al. Segmentation of dental cone‐beam CT scans affected by metal artifacts using a mixed‐scale dense convolutional neural network. Med Phys 2019;46:5027-35.
10.1002/mp.1379331463937PMC6900023
20 
Duong DQ, Nguyen KT, Kaipatur NR, Lou EHM, Noga M, Major PW, et al. Fully automated segmentation of alveolar bone using deep convolutional neural networks from intraoral ultrasound images. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2019. p.6632-5.
10.1109/EMBC.2019.885706031947362
21 
De Tobel J, Radesh P, Vandermeulen D, Thevissen PW. An automated technique to stage lower third molar development on panoramic radiographs for age estimation: a pilot study. J Forensic Odontostomatol 2017;35:42-54.
22 
Du X, Chen Y, Zhao J, Xi Y. A convolutional neural network based auto-positioning method for dental arch in rotational panoramic radiography. In: 2018 40th Annu Int Conf IEEE Eng Med Biol Soc (EMBC). IEEE, 2018. p.2615-8.
10.1109/EMBC.2018.851273230440944
23 
Liang K, Zhang L, Yang Y, Yang H, Xing Y. A self-supervised deep learning network for low-dose CT reconstruction. In: 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC). 2018. p.1-4.
10.1109/NSSMIC.2018.8824600
24 
Hu Z, Jiang C, Sun F, Zhang Q, Ge Y, Yang Y, et al. Artifact correction in low-dose dental CT imaging using Wasserstein generative adversarial networks. Med Phys 2019;46:1686-96.
10.1002/mp.1341530697765
25 
Hegazy MAA, Cho MH, Cho MH, Lee SY. U-net based metal segmentation on projection domain for metal artifact reduction in dental CT. Biomed Eng Lett 2019;9:375-85.
10.1007/s13534-019-00110-231456897PMC6694350
26 
Dinkla AM, Florkow MC, Maspero M, Savenije MHF, Zijlstra F, Doornaert PAH, et al. Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch-based threedimensional convolutional neural network. Med Phys 2019;46:4095-104.
10.1002/mp.1366331206701
27 
Hatvani J, Basarab A, Tourneret J, Gyöngy M, Kouamé D. A tensor factorization method for 3-D super resolution with application to dental CT. IEEE Trans Med Imaging 2019;38:1524-31.
10.1109/TMI.2018.288351730507496
28 
Hatvani J, Horváth A, Michetti J, Basarab A, Kouamé D, Gyöngy M. Deep learning-based superresolution applied to dental computed tomography. IEEE Trans Radiat Plasma Med Sci 2019;3:120-8.
10.1109/TRPMS.2018.2827239
29 
Kumar P, Srivastava MM. Example mining for incremental learning in medical imaging. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI). 2018. p.48-51.
10.1109/SSCI.2018.8628895
30 
Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learningbased convolutional neural network algorithm. J Dent 2018;77:106-11.
10.1016/j.jdent.2018.07.01530056118
31 
Casalegno F, Newton T, Daher R, Abdelaziz M, Lodi-Rizzini A, Schürmann F, et al. Caries detection with near-infrared transillumination using deep learning. J Dent Res 2019;98:1227-33.
10.1177/002203451987188431449759PMC6761787
32 
Moutselos K, Berdouses E, Oulis C, Maglogiannis I. Recognizing occlusal caries in dental intraoral images using deep learning. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2019. p.1617-20.
10.1109/EMBC.2019.885655331946206
33 
Prajapati SA, Nagaraj R, Mitra S. Classification of dental diseases using CNN and transfer learning. 2017 5th Int Symp Comput Bus Intell. IEEE, 2017. p.70-4.
10.1109/ISCBI.2017.8053547
34 
Liu L, Xu J, Huan Y, Zou Z, Yeh S-C, Zheng L-R. A smart dental health-IoT platform based on intelligent hardware, deep learning and mobile terminal. IEEE J Biomed Heal Informatics 2019;24:898-906.
10.1109/JBHI.2019.291991631180873
35 
Bezruk V, Krivenko S, Kryvenko L. Salivary lipid peroxidation and periodontal status detection in ukrainian atopic children with convolutional neural networks. In: 2017 4th International ScientificPractical Conference Problems of Infocommunications. Science and Technology (PIC S&T). 2017. p.122-4.
10.1109/INFOCOMMST.2017.8246364
36 
Aberin STA, De Goma JC. Detecting periodontal disease using convolutional neural networks. In: 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM). 2018. p.1-6.
10.1109/HNICEM.2018.8666389
37 
Joo J, Jeong S, Jin H, Lee U, Yoon JY, Kim SC. Periodontal disease detection using convolutional neural networks. In: 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). 2019. p.360-2.
10.1109/ICAIIC.2019.866902130782509
38 
Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, et al. Deep learning for the radiographic detection of periodontal bone loss. Sci Rep 2019;9:1-6.
10.1038/s41598-019-44839-331186466PMC6560098
39 
Uthoff RD, Song B, Sunny S, Patrick S, Suresh A, Kolur T, et al. Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for lowresource communities. PLoS One. 2018;13:e0207493.
10.1371/journal.pone.020749330517120PMC6281283
40 
Aubreville M, Knipfer C, Oetter N, Jaremenko C, Rodner E, Denzler J. Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning. Sci Rep 2017;7:1-10.
10.1038/s41598-017-12320-828931888PMC5607286
41 
Forslid G, Wieslander H, Bengtsson E, Wahlby C, Hirsch J-M, Stark CR, et al. Deep convolutional neural networks for detecting cellular changes due to malignancy. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). 2017. p.82-9.
10.1109/ICCVW.2017.18
42 
Das DK, Bose S, Maiti AK, Mitra B, Mukherjee G, Dutta PK. Automatic identification of clinically relevant regions from oral tissue histological images for oral squamous cell carcinoma diagnosis. Tissue Cell 2018;53:111-9.
10.1016/j.tice.2018.06.00430060821
43 
Jeyaraj PR, Samuel Nadar ER. Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm. J Cancer Res Clin Oncol 2019;145:829-37.
10.1007/s00432-018-02834-730603908
44 
Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, et al. Deep learning for the radiographic detection of apical lesions. J Endod 2019;45:917-22.e5.
10.1016/j.joen.2019.03.01631160078
45 
Murata M, Ariji Y, Ohashi Y, Kawai T, Fukuda M, Funakoshi T, et al. Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiol 2019;35:301-7.
10.1007/s11282-018-0363-730539342
46 
De Dumast P, Mirabel C, Cevidanes L, Ruellas A, Yatabe M, Ioshida M, et al. A web-based system for neural network based classification in temporomandibular joint osteoarthritis. Comput Med Imaging Graph 2018;67:45-54.
10.1016/j.compmedimag.2018.04.00929753964PMC5987251
47 
Kise Y, Ikeda H, Fujii T, Fukuda M, Ariji Y, Fujita H, et al. Preliminary study on the application of deep learning system to diagnosis of Sjögren's syndrome on CT images. Dentomaxillofac Radiol 2019;48:20190019.
10.1259/dmfr.2019001931075042PMC6747436
48 
Chu P, Bo C, Liang X, Yang J, Megalooikonomou V, Yang F, et al. Using octuplet siamese network for osteoporosis analysis on dental panoramic radiographs. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2018. p.2579-82.
10.1109/EMBC.2018.851275530440935
49 
Kats L, Vered M, Zlotogorski-Hurvitz A, Harpaz I. Atherosclerotic carotid plaque on panoramic radiographs: neural network detection. Int J Comput Dent 2019;22:163-9.
50 
Murata S, Lee C, Tanikawa C, Date S. Towards a fully automated diagnostic system for orthodontic treatment in dentistry. In: 2017 IEEE 13th International Conference on e-Science (e-Science). 2017. p.1-8.
10.1109/eScience.2017.12
51 
Patcas R, Bernini DAJ, Volokitin A, Agustsson E, Rothe R, Timofte R. Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age. Int J Oral Maxillofac Surg 2019;48:77-83.
10.1016/j.ijom.2018.07.01030087062
52 
Leonardi RM, Giordano D, Maiorana F, Greco M. Accuracy of cephalometric landmarks on monitordisplayed radiographs with and without image emboss enhancement. Eur J Orthod 2010;32: 242-7.
10.1093/ejo/cjp12220022892
53 
Qian J, Cheng M, Tao Y, Lin J, Lin H. CephaNet: an improved faster R-CNN for cephalometric landmark detection. 2019 IEEE 16th Int Symp Biomed Imaging (ISBI 2019). 2019. p.868-71.
10.1109/ISBI.2019.875943729846702
54 
Torosdagli N, Liberton DK, Verma P, Sincan M, Lee JS, Bagci U. Deep geodesic learning for segmentation and anatomical landmarking. IEEE Trans Med Imaging 2019;38:919-31.
10.1109/TMI.2018.287581430334750PMC6475529
55 
Shen Z, Shang X, Li Y, Bao Y, Zhang Xj, Dong X, et al. PredNet and CompNet: prediction and highprecision compensation of in-plane shape deformation for additive manufacturing. In: 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE). 2019. p.462-7.
10.1109/COASE.2019.8842894PMC6803195
56 
Yamaguchi S, Lee C, Karaer O, Ban S, Mine A, Imazato S. Predicting the debonding of CAD/CAM composite resin crowns with AI. J Dent Res 2019;98:1234-8.
10.1177/002203451986764131379234
57 
Zhang B, Dai N, Tian S, Yuan F, Yu Q. The extraction method of tooth preparation margin line based on S‐Octree CNN. Int J Numer Method Biomed Eng 2019;35:e3241.
10.1002/cnm.3241
58 
Zhao M, Xiong G, Shang X, Liu C, Shen Z, Wu H. Nonlinear deformation prediction and compensation for 3D printing based on CAE neural networks. In: 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE). 2019. p.667-72.
10.1109/COASE.2019.8843210
59 
Milošević D, Vodanović M, Galić I, Subašić M. Estimating biological gender from panoramic dental X-ray images. In: 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA). 2019. p.105-10.
10.1109/ISPA.2019.8868804
60 
Ilić I, Vodanović M, Subašić M. Gender estimation from panoramic dental X-ray images using deep convolutional networks. In: IEEE EUROCON 2019 -18th International Conference on Smart Technologies. 2019. p.1-5.
10.1109/EUROCON.2019.8861726
61 
Ali H, Khursheed M, Fatima SK, Shuja SM, Noor S. Object recognition for dental instruments using SSD-MobileNet. In: 2019 International Conference on Information Science and Communication Technology (ICISCT). 2019. p.1-6.
10.1109/CISCT.2019.8777441
62 
Luo C, Feng X, Chen J, Li J, Xu W, Li W, et al. Brush like a dentist: accurate monitoring of toothbrushing via wrist-worn gesture sensing. In: IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. 2019. p.1234-42.
10.1109/INFOCOM.2019.8737513
63 
Chollet F. Deep learning mit python und keras: das praxis-handbuch vom entwickler der kerasbibliothek. MITP-Verlags GmbH & Co. KG. 2018.
64 
Mcculloch WS, Pitts W. A logical calculus nervous activity. Bull Math Biol 1990;52:99-115.
10.1016/S0092-8240(05)80006-0
65 
Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 1958;65:386-408.
10.1037/h004251913602029
66 
Minsky M, Papert S. Perceptron: an introduction to computational geometry. MIT press; 2017.
10.7551/mitpress/11301.001.0001
67 
Werbos PJ. The roots of backpropagation: from ordered derivatives to neural networks and political forecasting. John Wiley & Sons; 1994.
68 
Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature. 1986;323:533-6.
10.1038/323533a0
69 
Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics. 2011. p.315-23.
70 
Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput 2006;18:1527-54.
10.1162/neco.2006.18.7.152716764513
71 
Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics. 2010. p.249-56.
72 
He K, Zhang X, Ren S, Sun J. Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision. 2015. p.1026-34.
10.1109/ICCV.2015.123
73 
Takaya N, Yuan C, Chu B, Saam T, Underhill H, Cai J, et al. Association between carotid plaque characteristics and subsequent ischemic cerebrovascular events: a prospective assessment with MRI - initial results. Stroke 2006;37:818-23.
10.1161/01.STR.0000204638.91099.9116469957
74 
Eun H, Kim C. Oriented tooth localization for periapical dental X-ray images via convolutional neural network. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). 2016. p.1-7.
10.1109/APSIPA.2016.7820720
75 
Oktay AB. Tooth detection with convolutional neural networks. 2017 Med Technol Natl Conf TIPTEKNO 2017. 2017. p.1-4.
76 
Miki Y, Muramatsu C, Hayashi T, Zhou X, Hara T, Katsumata A, et al. Classification of teeth in cone-beam CT using deep convolutional neural network. Comput Biol Med 2017;80:24-9.
10.1016/j.compbiomed.2016.11.00327889430
77 
Koch TL, Perslev M, Igel C, Brandt SS. Accurate segmentation of dental panoramic radiographs with U-nets. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). 2019. p.15-9.
10.1109/ISBI.2019.8759563
78 
Hiraiwa T, Ariji Y, Fukuda M, Kise K, 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 2019;48:20180218.
10.1259/dmfr.2018021830379570PMC6476355
79 
Vinayahalingam S, Xi T, Berge S, Maal T, de Jong G. Automated detection of third molars and mandibular nerve by deep learning. Sci Rep 2019;9:1-7.
10.1038/s41598-019-45487-331227772PMC6588560
80 
Yauney G, Angelino K, Edlund D, Shah P. Convolutional neural network for combined classification of fluorescent biomarkers and expert annotations using white light images. In: 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE). 2017. p.303-9.
10.1109/BIBE.2017.00-37
81 
Rana A, Yauney G, Wong LC, Gupta O, Muftu A, Shah P. Automated segmentation of gingival diseases from oral images. In: 2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT). 2017. p.144-7.
10.1109/HIC.2017.822760529234688PMC5717502
82 
Yang J, Xie Y, Liu L, Xia B, Cao Z, Guo C. Automated dental image analysis by deep learning on small dataset. In: 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC). 2018. p.492-7.
10.1109/COMPSAC.2018.00076
83 
Song T, Landini G, Fouad S, Mehanna H. Epithelial segmentation from in situ hybridisation histological samples using a deep central attention learning approach. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). 2019. p.1527-31.
10.1109/ISBI.2019.875938431535160PMC6768892
84 
Shen Z, Shang X, Zhao M, Dong X, Xiong G, Wang F-Y. A learning-based framework for error compensation in 3d printing. IEEE Trans Cybern 2019;49:4042-50.
10.1109/TCYB.2019.289855330843813
85 
Alarifi A, AlZubi AA. Memetic search optimization along with genetic scale recurrent neural network for predictive rate of implant treatment. J Med Syst 2018;42:202.
10.1007/s10916-018-1051-130225666
Information
  • Publisher :The Korean Academy of Oral & Maxillofacial Implantology
  • Publisher(Ko) :대한구강악안면임플란트학회
  • Journal Title :IMPLANTOLOGY
  • Journal Title(Ko) :대한구강악안면임플란트학회지
  • Volume : 24
  • No :3
  • Pages :148-181
  • Received Date :2020. 06. 09
  • Revised Date :2020. 07. 25
  • Accepted Date : 2020. 07. 29