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Yıl 2021, Cilt: 38 Sayı: 3s, 188 - 194, 09.05.2021

Öz

Kaynakça

  • Akçam, M.O., Takada, K, 2002. Fuzzy modelling for selecting headgear types. Eur J Orthod. Feb;24(1):99-106.
  • Alabi, R.O., Elmusrati, M., Sawazaki-Calone, I., Kowalski, L.P., Haglund, C., Coletta, R.D., Mäkitie, A., Salo, T., Leivo, I., Almangush, A., 2019. Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a Web-based prognostic tool. Virchows Arch. Oct;475(4):489-497. doi: 10.1007/s00428-019-02642-5
  • Aliaga, I.J., Vera, V., De Paz, J.F., García, A.E., Mohamad, M.S., 2015. Modelling the longevity of dental restorations by means of a CBR system. Biomed Res Int. 540306. doi:10.1155/2015/540306
  • Baliga, M.S., 2019. Artificial intelligence - The next frontier in pediatric dentistry. J Indian Soc Pedod Prev Dent. 37:315.
  • Bas, B., Özgönenel, O., Özden, B., Bekçioğlu, B., Bulut, E., Kurt, M., 2012. Use of arti- ficial neural network in differentiation of subgroups of temporoman- dibular internal derangements: A preliminary study. J Oral Maxillofac Surg. 70(1):51–59. doi:10.1016/j.joms.2011.03.069
  • Borza, D., Darabant, A., Danescu, R., 2018 Automatic Skin Tone Extraction for Visagism Applications. DOI: 10.5220/0006711104660473 In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP, pages 466-473
  • Bouletreau, P., Makaremi, P., Ibrahim, B., Louvrier, A., Sigaux, N., 2019. Artificial Intelligence: Applications in orthognathic surgery. J Stomatol Oral Maxillofac Surg. 120: 347–354 https://doi.org/10.1016/j.jormas.2019.06.001
  • Burt, J.R., Torosdagli, N., Khosravan, N., RaviPrakash, H., Mortazi, A., Tissavirasingham, F. et al., 2018. Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks. Br J Radiol. 91: 20170545.
  • Chen, Q., Wu, J., Li, S., Lyu, P., Wang, Y., Li, M., 2016. An ontology-driven, case- based clinical decision support model for removable partial den- ture design. Sci Rep. 6:27855. doi:10.1038/srep27855
  • Cheng, C., Cheng, X., Dai, N., Jiang, X., Sun, Y., Li, W., 2015. Prediction of facial deformation after complete denture prosthesis using BP neural network. Comput Biol Med. Nov 1;66:103-12. doi: 10.1016/j.compbiomed.2015.08.018.
  • Cho, J., Lee, K., Shin, E., Choy, G., Do, S., 2016. How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? arXiv:1511.06348 [Preprint]. [cited 2018 Nov 20] Available from: https://arxiv.org/abs/1511.06348
  • Devito, K.L., de Souza Barbosa, F., Felippe Filho, W.N., 2008. An artificial multilayer perceptron neural network for diagnosis of proximal dental caries. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. Dec;106(6):879-84. doi: 10.1016/j.tripleo.2008.03.002.
  • Ekert, T., Krois, J., Meinhold, L., Elhennawy, K., Emara, R., Golla, T., Schwendicke, F., 2019. Deep Learning for the Radiographic Detection of Apical Lesions. J Endod. Jul;45(7):917-922.e5. doi: 10.1016/j.joen.2019.03.016
  • Fazal, M.I., Patel, M.E., Tye, J., Gupta, Y., 2018. The past, present and future role of artificial intelligence in imaging. Eur J Radiol. 105: 246–50. doi: https:// doi. org/ 10. 1016/ j. ejrad. 2018. 06. 020
  • Goldhahn, J., Rampton-Branco-Weiss, V., Spinas, G.A., 2018. Could artificial intelligence make doctors obsolete? BMJ. 363, k4563.
  • Haidan, A.A., Abu-Hammad, O., Dar-Odeh, N., 2014. Predicting Tooth Surface Loss Using Genetic Algorithms-Optimized Artificial Neural Networks. Comput Math Methods Med. 106236.
  • Hashimoto, D.A., Rosman, G., Rus, D., Meireles, O.R., 2018. Artificial intelligence in surgery: promises and perils. Ann Surg. 268: 70–6. doi: https:// doi. org/ 10. 1097/ SLA. 0000000000002693
  • Herrera, L.J., Pulgar, R., Santana, J., Cardona, J.C., Guillen, A., Rojas, I., Perez Mdel, M., 2010. Prediction of color change after tooth bleaching using fuzzy logic for Vita Classical shades identification. Appl Opt. Jan 20;49(3):422-9. doi: 10.1364/AO.49.000422.
  • Javed, S., Zakirulla, M., Baig, R.U., Asif, S.M., Meer, A.B., 2020. Development of artificial neural network model for prediction of post-streptococcus mutans in dental caries. Comput Methods Programs Biomed. Apr;186:105198. doi: 10.1016/j.cmpb.2019.105198.
  • Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S. et al, 2017. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2: 230–43. doi: https:// doi. org/ 10. 1136/ svn- 2017- 000101
  • Jones, K.H., Laurie, G., Stevens, L., Dobbs, C., Ford, D.V., Lea, N., 2017. The other side of the coin: Harm due to the non-use of health-related data. Int. J. Med. Inform. 97, 43–51.
  • Jung, SK., Kim, T.W., 2016. New approach for the diagnosis of extractions with neural network machine learning. Am J Orthod Dentofacial Orthop. Jan;149(1):127-33. doi: 10.1016/j.ajodo.2015.07.030.
  • Kaplan, A., Haenlein, M., 2019. Siri, Siri, in my hand: who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Bus Horiz. 62(1):15–25. http://dx.doi.org/10.1016/j.bushor.2018.08.004.
  • Kim, D.W., Kim, H., Nam, W., Kim, H.J., Cha, I.H., 2018. Machine learning to predict the occurrence of bisphosphonate- related osteonecrosis of the jaw associated with dental extraction: A preliminary report. Bone. Nov;116:207-214. doi: 10.1016/j.bone.2018.04.020
  • Kim, J., Lee, H.S., Song, I.S., Jung, K.H., 2019. Dentnet: Deep neural transfer network for the detection of periodontal bone loss using panoramic dental radiographs. Sci Rep. Nov 26;9(1):17615. doi: 10.1038/s41598-019-53758-2.
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  • Kohli, M., Prevedello, L.M., Filice, R.W., Geis, J.R., 2017. Implementing machine learning in radiology practice and research. AJR Am J Roentgenol. 208: 754–60. doi: https:// doi. org/ 10. 2214/ AJR.16. 17224
  • Kök, H., Acilar, A.M., İzgi, M.S., 2019. Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics. Progress in Orthodontics. 20:41 https://doi.org/10.1186/s40510-019-0295-8
  • Kositbowornchai, S., Plermkamon, S., Tangkosol, T., 2013. Performance of an artificial neural network for vertical root fracture detection: An ex vivo study. Dent Traumatol. 29(2):151–155. doi:10.1111/j.1600- 9657.2012.01148.x
  • Kunz, F., Stellzig-Eisenhauer, A., Zeman, F., Boldt, J., 2020. Artificial intelligence in orthodontics: Evaluation of a fully automated cephalometric analysis using a customized convolutional neural network. J Orofac Orthop. Jan;81(1):52-68. doi: 10.1007/s00056-019-00203-8.
  • Lee, J.H., Kim, D.H., Jeong, S.N., Choi,. SH., 2018. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci. Apr;48(2):114-123 https://doi.org/10.5051/jpis.2018.48.2.114
  • Lee, K.S., Jung, S.K., Ryu, J.J., Shin, S.W., Choi, J., 2020. Evaluation of Transfer Learning with Deep Convolutional Neural Networks for Screening Osteoporosis in Dental Panoramic Radiographs. J. Clin. Med. 9, 392; doi:10.3390/jcm9020392
  • Li, H., Lai, L., Chen, L., Lu, C., Cai, Q., 2015. The prediction in computer color matching of dentistry based on GA+BP neural network. Comput Math Methods Med. 816719. doi:10.1155/2015/816719
  • Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M. et al, 2017. A survey on deep learning in medical image analysis. Med Image Anal. 42: 60–88. doi: 10.1016/ j. media. 2017. 07. 005
  • Mallishery, S., Chhatpar, P., Banga, K.S., Shah, T., Gupta, P., 2019. The precision of case difficulty and referral decisions: an innovative automated approach. Clin Oral Investig. Aug 13. doi: 10.1007/s00784-019-03050-4.
  • Mangano, F., Margiani, B., Admakin, O., 2019. A Novel Full-Digital Protocol (SCAN- PLAN-MAKE-DONE®) for the Design and Fabrication of Implant-Supported Monolithic Translucent Zirconia Crowns Cemented on Customized Hybrid Abutments: A Retrospective Clinical Study on 25 Patients. Int J Environ Res Public Health. 16(3).
  • Miyazaki, T., Hotta, Y., 2011. CAD/CAM systems available for the fabrication of crown and bridge restorations. Aust. Dent. J. 56 (Suppl. 1), 97–106.
  • Mupparapu, M., Wu, C.W., Chen, Y.C., 2018. Artificial intelligence, machine learning, neural networks, and deep learning: futuristic concepts for new dental diagnosis. Quintessence Int. 49: 687-8.
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Artificial Intelligence Technologies in Dentistry

Yıl 2021, Cilt: 38 Sayı: 3s, 188 - 194, 09.05.2021

Öz

One of the most important actors in the digitization process of our age has been the applications of artificial intelligence (AI). While the weak and strong AI sub-concepts and the different AI models within them are being utilized in many fields such as education, industry and medicine today, the interest of the dentistry field, which has started its integration into the digital world with CAD/CAM technology, in AI is increasing day by day. In different branches of dentistry; AI provides services to clinicians and researchers in many fields such as disease diagnosis, evaluation of the occurrence or recurrence of diseases such as oral cancer, and prediction of success in surgical and prosthetic treatments. In this article, studies in which AI models such as machine learning, convolutional neural network have found research and usage areas on the basis of different branches of dentistry are reviewed.

Kaynakça

  • Akçam, M.O., Takada, K, 2002. Fuzzy modelling for selecting headgear types. Eur J Orthod. Feb;24(1):99-106.
  • Alabi, R.O., Elmusrati, M., Sawazaki-Calone, I., Kowalski, L.P., Haglund, C., Coletta, R.D., Mäkitie, A., Salo, T., Leivo, I., Almangush, A., 2019. Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a Web-based prognostic tool. Virchows Arch. Oct;475(4):489-497. doi: 10.1007/s00428-019-02642-5
  • Aliaga, I.J., Vera, V., De Paz, J.F., García, A.E., Mohamad, M.S., 2015. Modelling the longevity of dental restorations by means of a CBR system. Biomed Res Int. 540306. doi:10.1155/2015/540306
  • Baliga, M.S., 2019. Artificial intelligence - The next frontier in pediatric dentistry. J Indian Soc Pedod Prev Dent. 37:315.
  • Bas, B., Özgönenel, O., Özden, B., Bekçioğlu, B., Bulut, E., Kurt, M., 2012. Use of arti- ficial neural network in differentiation of subgroups of temporoman- dibular internal derangements: A preliminary study. J Oral Maxillofac Surg. 70(1):51–59. doi:10.1016/j.joms.2011.03.069
  • Borza, D., Darabant, A., Danescu, R., 2018 Automatic Skin Tone Extraction for Visagism Applications. DOI: 10.5220/0006711104660473 In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP, pages 466-473
  • Bouletreau, P., Makaremi, P., Ibrahim, B., Louvrier, A., Sigaux, N., 2019. Artificial Intelligence: Applications in orthognathic surgery. J Stomatol Oral Maxillofac Surg. 120: 347–354 https://doi.org/10.1016/j.jormas.2019.06.001
  • Burt, J.R., Torosdagli, N., Khosravan, N., RaviPrakash, H., Mortazi, A., Tissavirasingham, F. et al., 2018. Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks. Br J Radiol. 91: 20170545.
  • Chen, Q., Wu, J., Li, S., Lyu, P., Wang, Y., Li, M., 2016. An ontology-driven, case- based clinical decision support model for removable partial den- ture design. Sci Rep. 6:27855. doi:10.1038/srep27855
  • Cheng, C., Cheng, X., Dai, N., Jiang, X., Sun, Y., Li, W., 2015. Prediction of facial deformation after complete denture prosthesis using BP neural network. Comput Biol Med. Nov 1;66:103-12. doi: 10.1016/j.compbiomed.2015.08.018.
  • Cho, J., Lee, K., Shin, E., Choy, G., Do, S., 2016. How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? arXiv:1511.06348 [Preprint]. [cited 2018 Nov 20] Available from: https://arxiv.org/abs/1511.06348
  • Devito, K.L., de Souza Barbosa, F., Felippe Filho, W.N., 2008. An artificial multilayer perceptron neural network for diagnosis of proximal dental caries. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. Dec;106(6):879-84. doi: 10.1016/j.tripleo.2008.03.002.
  • Ekert, T., Krois, J., Meinhold, L., Elhennawy, K., Emara, R., Golla, T., Schwendicke, F., 2019. Deep Learning for the Radiographic Detection of Apical Lesions. J Endod. Jul;45(7):917-922.e5. doi: 10.1016/j.joen.2019.03.016
  • Fazal, M.I., Patel, M.E., Tye, J., Gupta, Y., 2018. The past, present and future role of artificial intelligence in imaging. Eur J Radiol. 105: 246–50. doi: https:// doi. org/ 10. 1016/ j. ejrad. 2018. 06. 020
  • Goldhahn, J., Rampton-Branco-Weiss, V., Spinas, G.A., 2018. Could artificial intelligence make doctors obsolete? BMJ. 363, k4563.
  • Haidan, A.A., Abu-Hammad, O., Dar-Odeh, N., 2014. Predicting Tooth Surface Loss Using Genetic Algorithms-Optimized Artificial Neural Networks. Comput Math Methods Med. 106236.
  • Hashimoto, D.A., Rosman, G., Rus, D., Meireles, O.R., 2018. Artificial intelligence in surgery: promises and perils. Ann Surg. 268: 70–6. doi: https:// doi. org/ 10. 1097/ SLA. 0000000000002693
  • Herrera, L.J., Pulgar, R., Santana, J., Cardona, J.C., Guillen, A., Rojas, I., Perez Mdel, M., 2010. Prediction of color change after tooth bleaching using fuzzy logic for Vita Classical shades identification. Appl Opt. Jan 20;49(3):422-9. doi: 10.1364/AO.49.000422.
  • Javed, S., Zakirulla, M., Baig, R.U., Asif, S.M., Meer, A.B., 2020. Development of artificial neural network model for prediction of post-streptococcus mutans in dental caries. Comput Methods Programs Biomed. Apr;186:105198. doi: 10.1016/j.cmpb.2019.105198.
  • Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S. et al, 2017. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2: 230–43. doi: https:// doi. org/ 10. 1136/ svn- 2017- 000101
  • Jones, K.H., Laurie, G., Stevens, L., Dobbs, C., Ford, D.V., Lea, N., 2017. The other side of the coin: Harm due to the non-use of health-related data. Int. J. Med. Inform. 97, 43–51.
  • Jung, SK., Kim, T.W., 2016. New approach for the diagnosis of extractions with neural network machine learning. Am J Orthod Dentofacial Orthop. Jan;149(1):127-33. doi: 10.1016/j.ajodo.2015.07.030.
  • Kaplan, A., Haenlein, M., 2019. Siri, Siri, in my hand: who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Bus Horiz. 62(1):15–25. http://dx.doi.org/10.1016/j.bushor.2018.08.004.
  • Kim, D.W., Kim, H., Nam, W., Kim, H.J., Cha, I.H., 2018. Machine learning to predict the occurrence of bisphosphonate- related osteonecrosis of the jaw associated with dental extraction: A preliminary report. Bone. Nov;116:207-214. doi: 10.1016/j.bone.2018.04.020
  • Kim, J., Lee, H.S., Song, I.S., Jung, K.H., 2019. Dentnet: Deep neural transfer network for the detection of periodontal bone loss using panoramic dental radiographs. Sci Rep. Nov 26;9(1):17615. doi: 10.1038/s41598-019-53758-2.
  • Kise, Y., Shimizu, M., Ikeda, H., Fujii, T., Kuwada, C., Nishiyama, M., Funakoshi, T., Ariji, Y., Fujita, H., Katsumata, A., Yoshiura, K., Ariji, E., 2020. Usefulness of a deep learning system for diagnosing Sjögren's syndrome using ultrasonography images. Dentomaxillofac Radiol. Mar;49(3):20190348. doi: 10.1259/dmfr.20190348
  • Kohli, M., Prevedello, L.M., Filice, R.W., Geis, J.R., 2017. Implementing machine learning in radiology practice and research. AJR Am J Roentgenol. 208: 754–60. doi: https:// doi. org/ 10. 2214/ AJR.16. 17224
  • Kök, H., Acilar, A.M., İzgi, M.S., 2019. Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics. Progress in Orthodontics. 20:41 https://doi.org/10.1186/s40510-019-0295-8
  • Kositbowornchai, S., Plermkamon, S., Tangkosol, T., 2013. Performance of an artificial neural network for vertical root fracture detection: An ex vivo study. Dent Traumatol. 29(2):151–155. doi:10.1111/j.1600- 9657.2012.01148.x
  • Kunz, F., Stellzig-Eisenhauer, A., Zeman, F., Boldt, J., 2020. Artificial intelligence in orthodontics: Evaluation of a fully automated cephalometric analysis using a customized convolutional neural network. J Orofac Orthop. Jan;81(1):52-68. doi: 10.1007/s00056-019-00203-8.
  • Lee, J.H., Kim, D.H., Jeong, S.N., Choi,. SH., 2018. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci. Apr;48(2):114-123 https://doi.org/10.5051/jpis.2018.48.2.114
  • Lee, K.S., Jung, S.K., Ryu, J.J., Shin, S.W., Choi, J., 2020. Evaluation of Transfer Learning with Deep Convolutional Neural Networks for Screening Osteoporosis in Dental Panoramic Radiographs. J. Clin. Med. 9, 392; doi:10.3390/jcm9020392
  • Li, H., Lai, L., Chen, L., Lu, C., Cai, Q., 2015. The prediction in computer color matching of dentistry based on GA+BP neural network. Comput Math Methods Med. 816719. doi:10.1155/2015/816719
  • Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M. et al, 2017. A survey on deep learning in medical image analysis. Med Image Anal. 42: 60–88. doi: 10.1016/ j. media. 2017. 07. 005
  • Mallishery, S., Chhatpar, P., Banga, K.S., Shah, T., Gupta, P., 2019. The precision of case difficulty and referral decisions: an innovative automated approach. Clin Oral Investig. Aug 13. doi: 10.1007/s00784-019-03050-4.
  • Mangano, F., Margiani, B., Admakin, O., 2019. A Novel Full-Digital Protocol (SCAN- PLAN-MAKE-DONE®) for the Design and Fabrication of Implant-Supported Monolithic Translucent Zirconia Crowns Cemented on Customized Hybrid Abutments: A Retrospective Clinical Study on 25 Patients. Int J Environ Res Public Health. 16(3).
  • Miyazaki, T., Hotta, Y., 2011. CAD/CAM systems available for the fabrication of crown and bridge restorations. Aust. Dent. J. 56 (Suppl. 1), 97–106.
  • Mupparapu, M., Wu, C.W., Chen, Y.C., 2018. Artificial intelligence, machine learning, neural networks, and deep learning: futuristic concepts for new dental diagnosis. Quintessence Int. 49: 687-8.
  • my.cerec.com, 2020. Skramstad M. Smile Design with Primescan and CEREC 5.0 Software. (Online) Avaliable at https://my.cerec.com/en-us/Topics/smile-design-with-primescan-and-cerec-5-0-software.html
  • Nakano, Y., Takeshita, T., Kamio, N., Shiota, S., Shibata, Y., Suzuki, N., Yoneda, M., Hirofuji, T., Yamashita, Y., 2014. Supervised machine learning-based classification of oral malodor based on the microbiota in saliva samples. Artif Intell Med. Feb;60(2):97-101. doi: 10.1016/j.artmed.2013.12.001
  • Nam, Y., Kim, H.G., Kho, H.S., 2018. Differential Diagnosis of Jaw Pain using Informatics Technology. J Oral Rehabil. Aug;45(8):581-588. doi: 10.1111/joor.12655.
  • Özden, F.O., Özgönenel, O., Özden, B., Aydoğdu, A., 2015. Diagnosis of periodontal diseases using different classification algorithms: A preliminary study. Clin Pract. Niger J. 18(3):416–421. doi:10.4103/1119- 3077.151785
  • Park, W.J., Park, J.B., 2018. History and application of artificial neural networks in dentistry. Eur J Dent. 12:594-601. DOI:10.4103/ejd.ejd_325_18
  • Patcas, R., Bernini, D.A.J., Volokitin, A., Agustsson, E., Rothe, R., Timofte, R., 2019. Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age. Int J Oral Maxillofac Surg. Jan;48(1):77-83. doi: 10.1016/j.ijom.2018.07.010.
  • Patcas, R., Timofte, R., Volokitin, A., Agustsson, E., Eliades, T., Eichenberger, M., Bornstein, M.M., 2019. Facial attractiveness of cleft patients: a direct comparison between artificial-intelligence-based scoring and conventional rater groups. European Journal of Orthodontics. 1–6 doi:10.1093/ejo/cjz007
  • Poedjiastoeti, W., Suebnukarn, S., 2018. Application of Convolutional Neural Network in the Diagnosis of Jaw Tumors. Healthc Inform Res. Jul;24(3):236-241. doi: 10.4258/hir.2018.24.3.236
  • Polášková, A., Feberová, J., Dostálová, T., Kříž, P., Seydlová, M., 2013. Clinical decision support system in dental implantology. MEFANET J. 1:11–14.
  • Rudd, K., Bertoncini, C., Hinders, M., 2009. Simulations of ultrasonographic periodontal probe using the finite integration technique. Open Acoust J. 2:1-19.
  • Saghiri, M.A., Asgar, K., Boukani, K.K., et al, 2012. A new approach for locating the minor apical foramen using an artificial neural network. Int Endod J. 45(3):257–265. doi:10.1111/j.1365-2591.2011.01970.x (a)
  • Saghiri, M.A., Garcia-Godoy, F., Gutmann. J.L., Lotfi, M., Asgar, K., 2012. The reliability of artificial neural network in locating minor apical foramen: A cadaver study. J Endod. 38(8):1130–1134. doi:10.1016/j.joen. 2012.05.004 (b)
  • Scerri, M., Grech, V., 2020. Artificial intelligence in medicine Early Hum Dev. Mar 20:105017. doi: 10.1016/j.earlhumdev.2020.105017.
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  • Scrobotă, I., Băciuț, G., Filip, A.G., Todor, B., Blaga, F., Băciuț, M.F., 2017. Application of fuzzy logic in oral cancer risk assessment. Iran J Public Health. 46(5):612– 619
  • Stehrer, R. et al, 2019. Machine learning based prediction of perioperative blood loss in orthognathic surgery, J Cranio Maxill Surg. 47(11): 1676-1681 https://doi.org/10.1016/j.jcms.2019.08.005
  • Thanathornwong, B., Suebnukarn, S., Ouivirach, K., 2016. Decision support system for predicting color change after tooth whitening. Comput Methods Programs Biomed. 125:88–93. doi:10.1016/j.cmpb.2015. 11.004
  • Thrall, J.H., Li, X., Li, Q., Cruz, C., Do. S,, Dreyer, K., et al, 2018. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol. 15(3 Pt B): 504–8. doi: 10.1016/j.jacr.2017.12.026
  • Witten, I.H., Frank, E., Hall, M.A., 2011. Data Mining: Practical Machine Learning Tools and Techniques. – Morgan Kaufmann, Burlington, MA.
  • www.dolphinimaging.it, 2020. Dolphin 3D Surgery™. (Online) Avaliable at https://dolphinimaging.it/products/3d/3d-surgery/
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  • Yamaguchi, S., Lee, C., Karaer, O., Ban, S., Mine, A., Imazato, S., 2019. Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI. J Dent Res. Oct;98(11):1234-1238. doi: 10.1177/0022034519867641.
  • Zhang, W., Li, J., Li, Z.B. et al, 2018. Predicting postoperative facial swelling following impacted mandibular third molars extraction by using artificial neural networks evaluation. Sci Rep. 8:12281.
Toplam 63 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Sağlık Kurumları Yönetimi
Bölüm Clinical Research
Yazarlar

Berkman Albayrak

Gökhan Özdemir

Yeşim Ölçer Us

Emir Yüzbaşıoğlu 0000-0001-5348-6954

Yayımlanma Tarihi 9 Mayıs 2021
Gönderilme Tarihi 23 Mayıs 2020
Kabul Tarihi 6 Aralık 2020
Yayımlandığı Sayı Yıl 2021 Cilt: 38 Sayı: 3s

Kaynak Göster

APA Albayrak, B., Özdemir, G., Ölçer Us, Y., Yüzbaşıoğlu, E. (2021). Artificial Intelligence Technologies in Dentistry. Journal of Experimental and Clinical Medicine, 38(3s), 188-194.
AMA Albayrak B, Özdemir G, Ölçer Us Y, Yüzbaşıoğlu E. Artificial Intelligence Technologies in Dentistry. J. Exp. Clin. Med. Mayıs 2021;38(3s):188-194.
Chicago Albayrak, Berkman, Gökhan Özdemir, Yeşim Ölçer Us, ve Emir Yüzbaşıoğlu. “Artificial Intelligence Technologies in Dentistry”. Journal of Experimental and Clinical Medicine 38, sy. 3s (Mayıs 2021): 188-94.
EndNote Albayrak B, Özdemir G, Ölçer Us Y, Yüzbaşıoğlu E (01 Mayıs 2021) Artificial Intelligence Technologies in Dentistry. Journal of Experimental and Clinical Medicine 38 3s 188–194.
IEEE B. Albayrak, G. Özdemir, Y. Ölçer Us, ve E. Yüzbaşıoğlu, “Artificial Intelligence Technologies in Dentistry”, J. Exp. Clin. Med., c. 38, sy. 3s, ss. 188–194, 2021.
ISNAD Albayrak, Berkman vd. “Artificial Intelligence Technologies in Dentistry”. Journal of Experimental and Clinical Medicine 38/3s (Mayıs 2021), 188-194.
JAMA Albayrak B, Özdemir G, Ölçer Us Y, Yüzbaşıoğlu E. Artificial Intelligence Technologies in Dentistry. J. Exp. Clin. Med. 2021;38:188–194.
MLA Albayrak, Berkman vd. “Artificial Intelligence Technologies in Dentistry”. Journal of Experimental and Clinical Medicine, c. 38, sy. 3s, 2021, ss. 188-94.
Vancouver Albayrak B, Özdemir G, Ölçer Us Y, Yüzbaşıoğlu E. Artificial Intelligence Technologies in Dentistry. J. Exp. Clin. Med. 2021;38(3s):188-94.