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All issues Volume 297 (2021) E3S Web Conf., 297 (2021) 01074 References
Open Access
Issue
E3S Web Conf.
Volume 297, 2021
The 4th International Conference of Computer Science and Renewable Energies (ICCSRE'2021)
Article Number 01074
Number of page(s) 9
DOI https://doi.org/10.1051/e3sconf/202129701074
Published online 22 September 2021
  1. W.J. Park and J.-B. Park, “History and application of artificial neural networks in dentistry,” European Journal of Dentistry, vol. 12, no. 4, pp. 594–601, 2018, doi: 10.4103/ejd.ejd_325_18. [CrossRef] [PubMed] [Google Scholar]
  2. M. Mupparapu, C.-W. Wu, and Y.-C. Chen, “Artificial intelligence, machine learning, neural networks, and deep learning: Futuristic concepts for new dental diagnosis,” Quintessence International (Berlin, Germany: 1985), vol. 49, no. 9, pp. 687–688, 2018, doi: 10.3290/j.qi.a41107. [Google Scholar]
  3. J.R. Burt et al., “Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks,” The British Journal of Radiology, vol. 91, no. 1089, Sep. 2018, doi: 10.1259/bjr.20170545. [Google Scholar]
  4. V. Rajaraman, “JohnMcCarthy — Father of artificial intelligence,” Resonance, vol. 19, no. 3, pp. 198–207, Mar. 2014, doi: 10.1007/s12045-014-0027-9. [Google Scholar]
  5. R. Bellman, An introduction to artificial intelligence: can computers think? San Francisco: Boyd & Fraser Pub. Co., 1978. [Google Scholar]
  6. S.B. Khanagar et al., “Developments, application, and performance of artificial intelligence in dentistry - A systematic review,” Journal of Dental Sciences, Jun. 2020, doi: 10.1016/jjds.2020.06.019. [Google Scholar]
  7. M.L. Brodie, “What Is Data Science?,” Applied Data Science, pp. 101–130, 2019, doi: 10.1007/978-3-030-11821-1_8. [Google Scholar]
  8. Y. Riahi and S. Riahi, “Big Data and Big Data Analytics: concepts, types and technologies,” International Journal of Research and Engineering, vol. 5, no. 9, pp. 524–528, Nov. 2018, doi: 10.21276/ijre.2018.5.9.5. [Google Scholar]
  9. P.I. Dorado-Diaz, J. Sampedro-Gomez, V. Vicente-Palacios, and P.L. Sanchez, “Applications of Artificial Intelligence in Cardiology. The Future is Already Here,” Revista Espanola de Cardiologia (English Edition), vol. 72, no. 12, pp. 1065–1075, Dec. 2019, doi: 10.1016/j.rec.2019.05.014. [Google Scholar]
  10. S.A. Kareem, P. Pozos-Parra, and N. Wilson, “An application of belief merging for the diagnosis of oral cancer,” Applied Soft Computing, vol. 61, pp. 1105–1112, Dec. 2017, doi: 10.1016/j.asoc.2017.01.055. [Google Scholar]
  11. A. Yaji, S. Prasad, and A. Pai, “ACTA SCIENTIFIC DENTAL SCIENCES (ISSN: 25814893) Artificial Intelligence in Dento-Maxillofacial Radiology,” https://actascientific.com/ASDS/pdf/ASDS-03-0423.pdf. [Google Scholar]
  12. J.R. Rabunal, J. Dorado. Artificial neural networks in real-life applications. IGI Global: Hershey (2005), pp. 166–346. [Google Scholar]
  13. A. Kalappanavar, S. Sneha, and R.G. Annigeri, “Artificial intelligence: A dentist’s perspective,” Journal of Medicine, Radiology, Pathology and Surgery, vol. 5, no. 2, pp. 2–4, 2018, doi: 10.15713/insjmrps.123. [Google Scholar]
  14. S. Asgari Taghanaki, K. Abhishek, J.P. Cohen, J. Cohen-Adad, and G. Hamarneh, “Deep semantic segmentation of natural and medical images: a review,” Artificial Intelligence Review, vol. 54, no. 1, pp. 137–178, Jan. 2021, doi: 10.1007/s10462-020-09854-1. [Google Scholar]
  15. R. Ren, H. Luo, C. Su, Y. Yao, and W. Liao, “Machine learning in dental, oral and craniofacial imaging: a review of recent progress,” PeerJ, vol. 9, p. e11451, May 2021, doi: 10.7717/peerj.11451. [CrossRef] [PubMed] [Google Scholar]
  16. E.Y. Kim, K.O. Lim, and H.S. Rhee, “Predictive modeling of dental pain using neural network,” Studies in Health Technology and Informatics, vol. 146, pp. 745–746, 2009, Available: https://pubmed.ncbi.nlm.nih.gov/19592958/. [PubMed] [Google Scholar]
  17. M. Nieri, A. Crescini, R. Rotundo, T. Baccetti, P. Cortellini, and G.P. Pini Prato, “Factors affecting the clinical approach to impacted maxillary canines: A Bayesian network analysis,” American Journal of Orthodontics and Dentofacial Orthopedics: Official Publication of the American Association of Orthodontists, Its Constituent Societies, and the American Board of Orthodontics, vol. 137, no. 6, pp. 755–762, Jun. 2010, doi: 10.1016/j.ajodo.2008.08.028. [Google Scholar]
  18. T. Käkilehto, S. Salo, and M. Larmas, “Data mining of clinical oral health documents for analysis of the longevity of different restorative materials in Finland,” International Journal of Medical Informatics, vol. 78, no. 12, pp. e68–74, Dec. 2009, doi: 10.1016/j.ijmedinf.2009.04.004. [CrossRef] [PubMed] [Google Scholar]
  19. P.M. Speight, A.E. Elliott, J.A. Jullien, M.C. Downer, and J.M. Zakzrewska, “The use of artificial intelligence to identify people at risk of oral cancer and precancer,” British Dental Journal, vol. 179, no. 10, pp. 382–387, Nov. 1995, doi: 10.1038/sj.bdj.4808932. [CrossRef] [PubMed] [Google Scholar]
  20. K.L. Devito, F. de Souza Barbosa, and W.N. Felippe Filho, “An artificial multilayer perceptron neural network for diagnosis of proximal dental caries,” Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontics, vol. 106, no. 6, pp. 879–884, Dec. 2008, doi: 10.1016/j.tripleo.2008.03.002. [Google Scholar]
  21. X. Xie, L. Wang, and A. Wang, “Artificial Neural Network Modeling for Deciding if Extactions Are Necessary Prior to Orthodontic Treatment,” The Angle Orthodontist, vol. 80, no. 2, pp. 262–266, Mar. 2010, doi: 10.2319/111608-588.1. [CrossRef] [PubMed] [Google Scholar]
  22. M.A. Saghiri et al., “A new approach for locating the minor apical foramen using an artificial neural network,” International Endodontic Journal, vol. 45, no. 3, pp. 257–265, Mar. 2012, doi: 10.1111/j.1365-2591.2011.01970.x. [CrossRef] [PubMed] [Google Scholar]
  23. S.-K. Jung and T.-W. Kim, “New approach for the diagnosis of extractions with neural network machine learning,” American Journal of Orthodontics and Dentofacial Orthopedics: Official Publication of the American Association of Orthodontists, Its Constituent Societies, and the American Board of Orthodontics, vol. 149, no. 1, pp. 127–133, Jan. 2016, doi: 10.1016/j.ajodo.2015.07.030. [Google Scholar]
  24. T.C. Nino-Sandoval, S.V. Guevara Pérez, F.A. Gonzalez, R.A. Jaque, and C. Infante-Contreras, “Use of automated learning techniques for predicting mandibular morphology in skeletal class I, II and III,” Forensic Science International, vol. 281, pp. 187.e1–187.e7, Dec. 2017, doi: 10.1016/j.forsciint.2017.10.004. [Google Scholar]
  25. H.J. Yu, S.R. Cho, M.J. Kim, W.H. Kim, J.W. Kim, and J. Choi, “Automated Skeletal Classification with Lateral Cephalometry Based on Artificial Intelligence,” Journal of Dental Research, vol. 99, no. 3, pp. 249–256, Mar. 2020, doi: 10.1177/0022034520901715. [CrossRef] [PubMed] [Google Scholar]
  26. V. Patil et al., “Artificial neural network for gender determination using mandibular morphometric parameters: A comparative retrospective study,” Cogent Engineering, vol. 7, no. 1, p. 1723783, Jan. 2020, doi: 10.1080/23311916.2020.1723783. [Google Scholar]
  27. S. Javed, M. Zakirulla, R.U. Baig, S.M. Asif, and A.B. Meer, “Development of artificial neural network model for prediction of poststreptococcus mutans in dental caries,” Computer Methods and Programs in Biomedicine, vol. 186, p. 105198, Apr. 2020, doi: 10.1016/j.cmpb.2019.105198. [CrossRef] [PubMed] [Google Scholar]
  28. T. Eodygowski, K. Szajek, and M. Wierszycki, “Optimization of dental implant using genetic algorithm,” undefined, 2009. https://www.semanticscholar.org/paper/Optimization-of-dental-implant-using-genetic-%C5%81odygowskiSzajek/4d80c478c1b71803aa95085724ab250785f1dc25. [Google Scholar]
  29. H. Li, L. Lai, L. Chen, C. Lu, and Q. Cai, “The Prediction in Computer Color Matching of Dentistry Based on GA+BP Neural Network,” Computational and Mathematical Methods in Medicine, Mar. 22, 2015. https://www.hindawi.com/journals/cmmm/2015/816719/. [Google Scholar]
  30. P. Tripathi, C. Malathy and M. Prabhakaran, “Genetic algorithms-based approach for dental caries detection using back propagation neural network,” Int J Recent Technol Eng. 2019;8 2277–3878. [Google Scholar]
  31. V.K. Mago, A. Mago, P. Sharma, and J. Mago, “Fuzzy logic based expert system for the treatment of mobile tooth,” Advances in Experimental Medicine and Biology, vol. 696, pp. 607–614, 2011, doi: 10.1007/978-1-4419-7046-6_62. [CrossRef] [PubMed] [Google Scholar]
  32. B. Ambara, D. Putra, and D. Rusjayanthi, “Fuzzy Expert System of Dental and Oral Disease with Certainty Factor,” International Journal of Computer Science Issues, vol. 14, no. 3, pp. 2230, May 2017, doi: 10.20943/01201703.2230. [Google Scholar]
  33. L.J. Herrera et al., “Prediction of color change after tooth bleaching using fuzzy logic for Vita Classical shades identification,” Applied Optics, vol. 49, no. 3, pp. 422–429, Jan. 2010, doi: 10.1364/AO.49.000422. [CrossRef] [PubMed] [Google Scholar]
  34. H. Eun and C. Kim, “Oriented tooth localization for periapical dental X-ray images via convolutional neural network,” 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2016, doi: 10.1109/APSIPA.2016.7820720. [Google Scholar]
  35. S. Imangaliyev, M.H. Veen, C. Volgenant, B. Keijser, W. Crielaard, and E. Levin, “Deep Learning for Classification of Dental Plaque Images,” Semantic Scholar, 2016. https://www.semanticscholar.org/paper/Deep-Learning-for-Classification-of-Dental-Plaque-Imangaliyev-Veen/ad48783363e3d4ed0c913256afca672a31f2fa77. [Google Scholar]
  36. M. Aubreville et al., “Automatic Classification of Cancerous Tissue in Laser endomicroscopy Images of the Oral Cavity using Deep Learning,” Scientific Reports, vol. 7, no. 1, p. 11979, Sep. 2017, doi: 10.1038/s41598-017-12320-8.J. [CrossRef] [PubMed] [Google Scholar]
  37. De Tobel, P. Radesh, D. Vandermeulen, and P.W. Thevissen, “An automated technique to stage lower third molar development on panoramic radiographs for age estimation: a pilot study,” The Journal of Forensic Odonto-Stomatology, vol. 35, no. 2, pp. 42–54, Dec. 2017, Available: https://pubmed.ncbi.nlm.nih.gov/29384736/. [PubMed] [Google Scholar]
  38. J.-H. Lee, D.-H. Kim, S.-N. Jeong, and S.-H. Choi, “Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm,” Journal of Dentistry, vol. 77, pp. 106111, Oct. 2018, doi: 10.1016/j.jdent.2018.07.015. [Google Scholar]
  39. G. Yauney, A. Rana, L.C. Wong, P. Javia, A. Muftu, and P. Shah, “Automated Process Incorporating Machine Learning Segmentation and Correlation of Oral Diseases with Systemic Health,” arXiv:1810.10664 [cs, q-bio, stat], Oct. 2018, Accessed: Jun. 14, 2021. [Online]. Available: https://arxiv.org/abs/1810.10664. [Google Scholar]
  40. R. Patcas, D.A.J. Bernini, A. Volokitin, E. Agustsson, R. Rothe, and R. Timofte, “Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age,” International Journal of Oral and Maxillofacial Surgery, vol. 48, no. 1, pp. 77–83, Jan. 2019, doi: 10.1016/j.ijom.2018.07.010.W. [CrossRef] [PubMed] [Google Scholar]
  41. Zhang, J. Li, Z.-B. Li, and Z. Li, “Predicting postoperative facial swelling following impacted mandibular third molars extraction by using artificial neural networks evaluation,” Scientific Reports, vol. 8, no. 1, Aug. 2018, doi: 10.1038/s41598-018-29934-1. [Google Scholar]
  42. F. Casalegno et al., “Caries Detection with NearInfrared Transillumination Using Deep Learning,” Journal of Dental Research, vol. 98, no. 11, pp. 1227–1233, Oct. 2019, doi: 10.1177/0022034519871884. [CrossRef] [PubMed] [Google Scholar]
  43. H. Chen et al., “A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films,” Scientific Reports, vol. 9, no. 1, p. 3840, Mar. 2019, doi: 10.1038/s41598-019-40414-y. [CrossRef] [PubMed] [Google Scholar]
  44. T. Ekert et al., “Deep Learning for the Radiographic Detection of Apical Lesions,” Journal of Endodontics, vol. 45, no. 7, pp. 917–922.e5, Jul. 2019, doi: 10.1016/jjoen.2019.03.016. [CrossRef] [PubMed] [Google Scholar]
  45. M. Fukuda et al., “Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography,” Oral Radiology, Sep. 2019, doi: 10.1007/s11282-019-00409-x. [Google Scholar]
  46. T. Hiraiwa et al., “A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography,” Dentomaxillo facial Radiology, vol. 48, no. 3, p. 20180218, Mar. 2019, doi: 10.1259/dmfr.20180218. [Google Scholar]
  47. J. Krois et al., “Deep Learning for the Radiographic Detection of Periodontal Bone Loss,” Scientific Reports, vol. 9, no. 1, Jun. 2019, doi: 10.1038/s41598-019-44839-3. [CrossRef] [Google Scholar]
  48. D.V. Tuzoff et al., “Tooth detection and numbering in panoramic radiographs using convolutional neural networks,” Dento Maxillo Facial Radiology, vol. 48, no. 4, p. 20180051, May 2019, doi: 10.1259/dmfr.20180051. [CrossRef] [PubMed] [Google Scholar]
  49. K.-S. Lee, S.-K. Jung, J.-J. Ryu, S.-W. Shin, and J. Choi, “Evaluation of Transfer Learning with Deep Convolutional Neural Networks for Screening Osteoporosis in Dental Panoramic Radiographs,” Journal of Clinical Medicine, vol. 9, no. 2, Feb. 2020, doi: 10.3390/jcm9020392. [Google Scholar]
  50. F. Schwendicke, K. Elhennawy, S. Paris, P. Friebertshäuser, and J. Krois, “Deep learning for caries lesion detection in near-infrared light transillumination images: A pilot study,” Journal of Dentistry, vol. 92, p. 103–260, Jan. 2020, doi: 10.1016/jjdent.2019.103260. [Google Scholar]
  51. S. Vinayahalingam, T. Xi, S. Bergé, T. Maal, and G. de Jong, “Automated detection of third molars and mandibular nerve by deep learning,” Scientific Reports, vol. 9, no. 1, p. 9007, Jun. 2019, doi: 10.1038/s41598-019-45487-3. [CrossRef] [PubMed] [Google Scholar]
  52. A.F. Leite, K. de F. Vasconcelos, H. Willems, and R. Jacobs, “Radiomics and Machine Learning in Oral Healthcare,” Proteomics. Clinical Applications, vol. 14, no. 3, p. e1900040, May 2020, doi: 10.1002/prca.201900040. [CrossRef] [PubMed] [Google Scholar]
  53. B. Thanathornwong, “Bayesian-Based Decision Support System for Assessing the Needs for Orthodontic Treatment,” Healthcare Informatics Research, vol. 24, no. 1, pp. 22–28, Jan. 2018, doi: 10.4258/hir.2018.24.1.22. [CrossRef] [PubMed] [Google Scholar]
  54. M. Johari, F. Esmaeili, A. Andalib, S. Garjani, and H. Saberkari, “Detection of vertical root fractures in intact and endodontically treated premolar teeth by designing a probabilistic neural network: an ex vivo study,” Dentomaxillofacial Radiology, vol. 46, no. 2, doi: 10.1259/dmfr.20160107. [Google Scholar]
  55. M. Feres, Y. Louzoun, S. Haber, M. Faveri, L.C. Figueiredo, and L. Levin, “Support vector machine-based differentiation between aggressive and chronic periodontitis using microbial profiles,” International Dental Journal, vol. 68, no. I. pp. 39–46, Feb. 2018, doi: 10.1111/idj.12326. [CrossRef] [PubMed] [Google Scholar]
  56. F. Schwendicke, T. Golla, M. Dreher, and J. Krois, “Convolutional neural networks for dental image diagnostics: A scoping review,” Journal of Dentistry, vol. 91, p. 103226, Dec. 2019, doi: 10.1016/jjdent.2019.103226. [CrossRef] [PubMed] [Google Scholar]
  57. M.A. Gianfrancesco, S. Tamang, J. Yazdany, and G. Schmajuk, “Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data,” JAMA Internal Medicine, vol. 178, no. 11, p. 1544, Nov. 2018, doi: 10.1001/jamainternmed.2018.3763. [CrossRef] [PubMed] [Google Scholar]
  58. J.R. England and P.M. Cheng, “Artificial Intelligence for Medical Image Analysis: A Guide for Authors and Reviewers,” American Journal of Roentgenology, vol. 212, no. 3, pp. 513–519, Mar. 2019, doi: 10.2214/ajr.18.20490. [Google Scholar]
  59. T.M. Maddox, J.S. Rumsfeld, and P.R.O. Payne, “Questions for Artificial Intelligence in Health Care,” JAMA, vol. 321, no. 1, p. 31, Jan. 2019, doi: 10.1001/jama.2018.18932. [CrossRef] [PubMed] [Google Scholar]
  60. A. Hosny, C. Parmar, J. Quackenbush, L.H. Schwartz, and H.J.W.L. Aerts, “Artificial intelligence in radiology,” Nature Reviews Cancer, vol. 18, no. 8, pp. 500–510, May 2018, doi: 10.1038/s41568-018-0016-5. [CrossRef] [PubMed] [Google Scholar]
  61. Chen, J. Elenee Argentinis, and G. Weber, “IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research,” Clinical Therapeutics, vol. 38, no. 4, pp. 688–701, Apr. 2016, doi: 10.1016/j.clinthera.2015.12.001. [CrossRef] [PubMed] [Google Scholar]
  62. F.A. Anifowose, “Artificial Intelligence Application in Reservoir Characterization and Modeling: Whitening the Black Box,” onepetro.org, Mar. 14, 2011. https://onepetro.org/SPEYPTS/proceedings-abstract/11YPTS/All-11YPTS/SPE-155413-MS/150902 [Google Scholar]

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