Review
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Diş Hekimliğinde Makine Öğrenimi Uygulamaları

Year 2022, Volume: 9 Issue: 3, 977 - 983, 26.12.2022

Güler Burcu Senirkentli , Gazi Erkan Bostancı , Mehmet Serdar Güzel , Metehan Unal

https://doi.org/10.15311/selcukdentj.1032041

Abstract

Yapay Zeka, diş hekimliği alanında karşılaşılan zorlu karar verme süreçlerini çözmek için yeni yaklaşımların kullanılabildiği tıp ve diş hekimliği dahil birçok alanda bir atılım olarak ortaya çıkmıştır. Artan nüfus ve buna bağlı olarak artan diş tedavi ihtiyaçlarını çözmek için yapay zeka bir karar destek mekanizması olarak kullanılabilir. Ayrıca uzman görüşü gerektiren teşhis ve tedavi planlama aşamalarında diş hekimlerine yardımcı olur. Bu mini inceleme, bu alandaki son çalışmalardan bazılarını kapsamakta ve diş problemlerinde makine öğreniminin kullanımına ilişkin gelecekteki yönergeleri öngörmektedir.

Keywords

Diş Hekimliği Makine Öğrenimi Uygulamaları Yapay zeka

References

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  • 3.Schwendicke, F. A., Samek, W., & Krois, J. (2020). Artificial intelligence in dentistry: chances and challenges. Journal of dental research, 99(7), 769-774.
  • 4.Shan, T., Tay, F. R., & Gu, L. (2020). Application of Artificial Intelligence in Dentistry. Journal of dental research, 0022034520969115.
  • 5.Wang H, Zhang H. Movie genre preference prediction using machine learning for customer-based information. In: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC) IEEE; 2018:110–6.
  • 6.Breiman L. Random forests. Machine Learning 2001;45(1):5–32.
  • 7.Denoeux T. A k-nearest neighbor classification rule based on Dempster-Shafer theory. In: Classic works of the Dempster-Shafer theory of belief functions Springer; 2008:737–60.
  • 8.Ramchoun H, Amine M, Idrissi J, Ghanou Y, Ettaouil M. Multilayer Perceptron: Architecture Optimization and Training. Int. J. Interact. Multimed. Artif. Intell. 2016;4(1):26. MLP.
  • 9.Kim, E. Y., Lim, K. O., & Rhee, H. S. (2009). Predictive modeling of dental pain using neural network. Studies in health technology and informatics, 146, 745-746.
  • 10.Jung, S. K., & Kim, T. W. (2016). New approach for the diagnosis of extractions with neural network machine learning. American Journal of Orthodontics and Dentofacial Orthopedics, 149(1), 127-133.
  • 11. Nilsson NJ. Artificial intelligence: a new synthesis. Morgan Kaufmann Publishers Inc, 1998:513.
  • 12.Mario MC, Abe JM, Ortega NR, Del Santo M Jr. Paraconsistent artificial neural network as auxiliary in cephalometric diagnosis. Artif Organs. 2010;34(7):E215–221. doi:10.1111/j.1525-1594.2010.00994.x.
  • 13.Ozden FO, Özgönenel O, Özden B, Aydogdu A, Niger J. Diagnosis of periodontal diseases using different classification algorithms: A preliminary study. Clin Pract. 2015;18(3):416–421. doi:10.4103/1119- 3077.151785.
  • 14.Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent. 2018 Oct;77:106-111. doi: 10.1016/j.jdent.2018.07.015. Epub 2018 Jul 26. PMID: 30056118.
  • 15.Brignardello-Petersen R. Artificial intelligence system seems to be able to detect a high proportion of periapical lesions in cone-beam computed tomographic images. J Am Dent Assoc. 2020 Sep;151(9):e83. doi: 10.1016/j.adaj.2020.04.006.
  • 16.Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, Özyürek T. 2020. Evaluation of artificial
intelligence for detecting periapical pathosis on cone-beam computed tomography scans.
International Endodontic Journal 53(5):680–689 DOI 10.1111/iej.13265.
  • 17.You W, Hao A, Li S, Wang Y, Xia B. 2020. Deep learning-based dental plaque detection on
primary teeth: a comparison with clinical assessments. BMC Oral Health 20(1):141
DOI 10.1186/s12903-020-01114-6.
  • 18.Lin C-C, Wu C-Z, Huang M-S, Huang C-F, Cheng H-C, Wang DP. 2020. Fully digital workflow for planning static guided implant surgery: a prospective accuracy study. Journal of Clinical
Medicine 9(4):980.
  • 19.Dahiya K, Kumar N, Bajaj P, Sharma A, Sikka R, Dahiya S. 2018. Qualitative assessment of
reliability of cone-beam computed tomography in evaluating bone density at posterior
mandibular implant site. The Journal of Contemporary Dental Practice 19(4):426–430
DOI 10.5005/jp-journals-10024-2278.
  • 20.Bas B, Ozgonenel O, Ozden B, Bekcioglu B, Bulut E, Kurt M. Use of artificial neural network in differentiation of subgroups of temporomandibular internal derangements: A preliminary study. J Oral Maxillofac Surg. 2012;70(1):51–59.
  • 21.Makaremi M, Lacaule C, Mohammad-Djafari A. Deep learning and artificial intelligence for the determination of the cervical vertebra maturation degree from lateral radiography. Entropy
2019;21:1222.
  • 22.Kök H, Acilar AM, _Izgi MS. Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics. Prog
Orthod 2019;20:41.
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717102.
  • 24.Moghimi S, Talebi M, Parisay I. Design and implementation of a hybrid genetic algorithm and artificial neural network system for predicting the sizes of unerupted canines and premolars. Eur J Orthod. 2012; 34(4):480–486. doi:10.1093/ejo/cjr042.
  • 25.Niño-Sandoval TC, Guevara Pérez SV, González FA, Jaque RA, Infante- Contreras C. Use of automated learning techniques for predicting mandibular morphology in skeletal class I, II and III. Forensic Sci Int. 2017;281:187.e1–187.e7. https://doi.org/10.1016/j.forsciint.2017. 10.004.
  • 26.Choi HI, Jung SK, Baek SH, et al. Artificial intelligent model with neural network machine learning for the diagnosis of orthognathic surgery. J Craniofac Surg 2019;30:1986e9.
  • 27.Jung SK, Kim TW. New approach for the diagnosis of extractions with neural network machine learning. Am J Orthod Dentofacial Orthop 2016;149:127e33.
  • 28.Patcas R, Bernini DAJ, Volokitin A, Agustsson E, Rothe R, Timofte R. 2019a. Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age. International Journal of Oral and Maxillofacial Surgery 48(1):77–83.
  • 29.Thanathornwong B, Suebnukarn S, Ouivirach K. Decision support system for predicting color change after tooth whitening. Comput Methods Programs Biomed. 2016;125:88–93. doi:10.1016/j.cmpb.2015. 11.004.
  • 30.Warnakulasuriya S. (2009). Global epidemiology of oral and oropharyngeal cancer. Oral Oncol, 45 (4–5): 309–16.
  • 31.Das N, Hussain E, Mahanta LB. 2020. Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional
neural network. Neural Networks 128(2):47–60.
  • 32.Chan CH, Huang TT, Chen CY, Lee CC, Chan MY, Chung PC. 2019. Texture-map-based
branch-collaborative network for oral cancer detection. IEEE Transactions on Biomedical
Circuits and Systems 13(4):766–780.
  • 33.Marsden M, Weyers BW, Bec J, Sun T, Gandour-Edwards RF, Birkeland AC, Abouyared M,
Bewley AF, Farwell DG, Marcu L. 2020. Intraoperative margin assessment in oral and oropharyngeal cancer using label-free fluorescence lifetime imaging and machine learning. IEEE
Transactions on Biomedical Engineering 68(3):857–868.
  • 34.Bur, A. M., Holcomb, A., Goodwin, S., Woodroof, J., Karadaghy, O., Shnayder, Y., ... & Shew, M. (2019). Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma. Oral oncology, 92, 20-25.
  • 35.Tseng YJ, Wang HY, Lin TW, Lu JJ, Hsieh CH, Liao CT. Development of a Machine Learning Model for Survival Risk Stratification of Patients With Advanced Oral Cancer. JAMA Netw Open. 2020 Aug 3;3(8):e2011768. doi: 10.1001/jamanetworkopen.2020.11768. PMID: 32821921; PMCID: PMC7442932.
  • 36.Chang SW, Abdul-Kareem S, Merican AF, Zain RB. Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods. BMC Bioinformatics. 2013 May 31;14:170. doi: 10.1186/1471-2105-14-170. PMID: 23725313; PMCID: PMC3673908.
  • 37.Ariji Y, Fukuda M, Kise Y, Nozawa M, Yanashita Y, Fujita H, Katsumata A, Ariji E. 2019.
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
Surgery, Oral Medicine, Oral Pathology and Oral Radiology 127(5):458–463.
  • 38.Scrobotă I, Băciuț G, Filip AG, Todor B, Blaga F, Băciuț MF. Application of fuzzy logic in oral cancer risk assessment. Iran J Public Health. 2017;46(5):612–619.
  • 39.Parewe, A. M. A. K., Mahmudy, W. F., Ramdhani, F., & Anggodo, Y. P. (2018). Dental disease detection using hybrid fuzzy logic and evolution strategies. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-8), 27-33.
  • 40.Hiraiwa T, Ariji Y, Fukuda M, Kise Y, Nakata K, Katsumata A, Fujita H, Ariji E. 2019. A
deep-learning artificial intelligence system for assessment of root morphology of the mandibular
first molar on panoramic radiography. Dentomaxillofacial Radiology 48(3):20180218.
  • 41.Saghiri MA, Garcia-Godoy F, Gutmann JL, Lotfi M, Asgar K. The reliability of artificial neural network in locating minor apical foramen: A cadaver study. J Endod. 2012;38(8):1130–1134. doi:10.1016/j.joen. 2012.05.004.
  • 42.Senirkentli, G. B., Sen, S., Farsak, O. and Bostanci, E., “A Neural Expert System Based Dental Trauma Diagnosis Application”, TIPTEKNO 2019, Aydin, Turkey, 2019.
  • 43.Senirkentli, G. B., Ekinci, F., Bostanci, E., Güzel, M. S., Dağli, Ö., Karim, A. M., & Mishra, A. (2021, February). Proton Therapy for Mandibula Plate Phantom. In Healthcare (Vol. 9, No. 2, p. 167). Multidisciplinary Digital Publishing Institute.
  • 44.Goh, W. P., Tao, X., Zhang, J., & Yong, J. (2016). Decision support systems for adoption in dental clinics: a survey. Knowledge-Based Systems, 104, 195-206.
  • 45.Currie G, Hawk KE, Rohren EM. 2020. Ethical principles for the application of artificial intelligence (AI) in nuclear medicine. Eur J Nucl Med Mol Imaging. 47(4):748–752.
  • 46.Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., ... & Lee, S. I. (2020). From local explanations to global understanding with explainable AI for trees. Nature machine intelligence, 2(1), 56-67.
  • 47.Zlotogorski-Hurvitz, A., Dekel, B. Z., Malonek, D., Yahalom, R., & Vered, M. (2019). FTIR-based spectrum of salivary exosomes coupled with computational-aided discriminating analysis in the diagnosis of oral cancer. Journal of cancer research and clinical oncology, 145(3), 685-694.
  • 48.Recht M, Bryan RN. Artificial Intelligence: Threat or Boon to Radiologists? J Am Coll Radiol. 2017 Nov;14(11):1476-1480. doi:10.1016/j.jacr.2017.07.007. Epub 2017 Aug 19. PMID: 28826960.

Machine Learning Applications in Dentistry

Year 2022, Volume: 9 Issue: 3, 977 - 983, 26.12.2022

Güler Burcu Senirkentli , Gazi Erkan Bostancı , Mehmet Serdar Güzel , Metehan Unal

https://doi.org/10.15311/selcukdentj.1032041

Abstract

Artificial Intelligence has emerged as a breakthrough in many fields including medicine and dentistry where new approaches can be employed to solve challenging decision making processes faced in the dental field. Artificial intelligence can be used as a decision support mechanism to solve the increasing population and consequently the increasing dental treatment needs. It also assists dentists in diagnosis and treatment planning stages that require expert opinion. This mini-review covers some of the recent studies in this area and envisions future directions on the use of machine learning in dental problems.

Keywords

Dentistry Machine Learning Applications

References

  • 1. Joshi AV. Machine learning and artificial intelligence. Springer; 2020.
  • 2.Park, W. J., & Park, J. B. (2018). History and application of artificial neural networks in dentistry. European journal of dentistry, 12(4), 594.
  • 3.Schwendicke, F. A., Samek, W., & Krois, J. (2020). Artificial intelligence in dentistry: chances and challenges. Journal of dental research, 99(7), 769-774.
  • 4.Shan, T., Tay, F. R., & Gu, L. (2020). Application of Artificial Intelligence in Dentistry. Journal of dental research, 0022034520969115.
  • 5.Wang H, Zhang H. Movie genre preference prediction using machine learning for customer-based information. In: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC) IEEE; 2018:110–6.
  • 6.Breiman L. Random forests. Machine Learning 2001;45(1):5–32.
  • 7.Denoeux T. A k-nearest neighbor classification rule based on Dempster-Shafer theory. In: Classic works of the Dempster-Shafer theory of belief functions Springer; 2008:737–60.
  • 8.Ramchoun H, Amine M, Idrissi J, Ghanou Y, Ettaouil M. Multilayer Perceptron: Architecture Optimization and Training. Int. J. Interact. Multimed. Artif. Intell. 2016;4(1):26. MLP.
  • 9.Kim, E. Y., Lim, K. O., & Rhee, H. S. (2009). Predictive modeling of dental pain using neural network. Studies in health technology and informatics, 146, 745-746.
  • 10.Jung, S. K., & Kim, T. W. (2016). New approach for the diagnosis of extractions with neural network machine learning. American Journal of Orthodontics and Dentofacial Orthopedics, 149(1), 127-133.
  • 11. Nilsson NJ. Artificial intelligence: a new synthesis. Morgan Kaufmann Publishers Inc, 1998:513.
  • 12.Mario MC, Abe JM, Ortega NR, Del Santo M Jr. Paraconsistent artificial neural network as auxiliary in cephalometric diagnosis. Artif Organs. 2010;34(7):E215–221. doi:10.1111/j.1525-1594.2010.00994.x.
  • 13.Ozden FO, Özgönenel O, Özden B, Aydogdu A, Niger J. Diagnosis of periodontal diseases using different classification algorithms: A preliminary study. Clin Pract. 2015;18(3):416–421. doi:10.4103/1119- 3077.151785.
  • 14.Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent. 2018 Oct;77:106-111. doi: 10.1016/j.jdent.2018.07.015. Epub 2018 Jul 26. PMID: 30056118.
  • 15.Brignardello-Petersen R. Artificial intelligence system seems to be able to detect a high proportion of periapical lesions in cone-beam computed tomographic images. J Am Dent Assoc. 2020 Sep;151(9):e83. doi: 10.1016/j.adaj.2020.04.006.
  • 16.Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, Özyürek T. 2020. Evaluation of artificial
intelligence for detecting periapical pathosis on cone-beam computed tomography scans.
International Endodontic Journal 53(5):680–689 DOI 10.1111/iej.13265.
  • 17.You W, Hao A, Li S, Wang Y, Xia B. 2020. Deep learning-based dental plaque detection on
primary teeth: a comparison with clinical assessments. BMC Oral Health 20(1):141
DOI 10.1186/s12903-020-01114-6.
  • 18.Lin C-C, Wu C-Z, Huang M-S, Huang C-F, Cheng H-C, Wang DP. 2020. Fully digital workflow for planning static guided implant surgery: a prospective accuracy study. Journal of Clinical
Medicine 9(4):980.
  • 19.Dahiya K, Kumar N, Bajaj P, Sharma A, Sikka R, Dahiya S. 2018. Qualitative assessment of
reliability of cone-beam computed tomography in evaluating bone density at posterior
mandibular implant site. The Journal of Contemporary Dental Practice 19(4):426–430
DOI 10.5005/jp-journals-10024-2278.
  • 20.Bas B, Ozgonenel O, Ozden B, Bekcioglu B, Bulut E, Kurt M. Use of artificial neural network in differentiation of subgroups of temporomandibular internal derangements: A preliminary study. J Oral Maxillofac Surg. 2012;70(1):51–59.
  • 21.Makaremi M, Lacaule C, Mohammad-Djafari A. Deep learning and artificial intelligence for the determination of the cervical vertebra maturation degree from lateral radiography. Entropy
2019;21:1222.
  • 22.Kök H, Acilar AM, _Izgi MS. Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics. Prog
Orthod 2019;20:41.
  • 23.Leonardi R, Giordano D, Maiorana F. An evaluation of cellular neural networks for the automatic identification of cephalometric landmarks on digital images. J Biomed Biotechnol 2009:
717102.
  • 24.Moghimi S, Talebi M, Parisay I. Design and implementation of a hybrid genetic algorithm and artificial neural network system for predicting the sizes of unerupted canines and premolars. Eur J Orthod. 2012; 34(4):480–486. doi:10.1093/ejo/cjr042.
  • 25.Niño-Sandoval TC, Guevara Pérez SV, González FA, Jaque RA, Infante- Contreras C. Use of automated learning techniques for predicting mandibular morphology in skeletal class I, II and III. Forensic Sci Int. 2017;281:187.e1–187.e7. https://doi.org/10.1016/j.forsciint.2017. 10.004.
  • 26.Choi HI, Jung SK, Baek SH, et al. Artificial intelligent model with neural network machine learning for the diagnosis of orthognathic surgery. J Craniofac Surg 2019;30:1986e9.
  • 27.Jung SK, Kim TW. New approach for the diagnosis of extractions with neural network machine learning. Am J Orthod Dentofacial Orthop 2016;149:127e33.
  • 28.Patcas R, Bernini DAJ, Volokitin A, Agustsson E, Rothe R, Timofte R. 2019a. Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age. International Journal of Oral and Maxillofacial Surgery 48(1):77–83.
  • 29.Thanathornwong B, Suebnukarn S, Ouivirach K. Decision support system for predicting color change after tooth whitening. Comput Methods Programs Biomed. 2016;125:88–93. doi:10.1016/j.cmpb.2015. 11.004.
  • 30.Warnakulasuriya S. (2009). Global epidemiology of oral and oropharyngeal cancer. Oral Oncol, 45 (4–5): 309–16.
  • 31.Das N, Hussain E, Mahanta LB. 2020. Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional
neural network. Neural Networks 128(2):47–60.
  • 32.Chan CH, Huang TT, Chen CY, Lee CC, Chan MY, Chung PC. 2019. Texture-map-based
branch-collaborative network for oral cancer detection. IEEE Transactions on Biomedical
Circuits and Systems 13(4):766–780.
  • 33.Marsden M, Weyers BW, Bec J, Sun T, Gandour-Edwards RF, Birkeland AC, Abouyared M,
Bewley AF, Farwell DG, Marcu L. 2020. Intraoperative margin assessment in oral and oropharyngeal cancer using label-free fluorescence lifetime imaging and machine learning. IEEE
Transactions on Biomedical Engineering 68(3):857–868.
  • 34.Bur, A. M., Holcomb, A., Goodwin, S., Woodroof, J., Karadaghy, O., Shnayder, Y., ... & Shew, M. (2019). Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma. Oral oncology, 92, 20-25.
  • 35.Tseng YJ, Wang HY, Lin TW, Lu JJ, Hsieh CH, Liao CT. Development of a Machine Learning Model for Survival Risk Stratification of Patients With Advanced Oral Cancer. JAMA Netw Open. 2020 Aug 3;3(8):e2011768. doi: 10.1001/jamanetworkopen.2020.11768. PMID: 32821921; PMCID: PMC7442932.
  • 36.Chang SW, Abdul-Kareem S, Merican AF, Zain RB. Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods. BMC Bioinformatics. 2013 May 31;14:170. doi: 10.1186/1471-2105-14-170. PMID: 23725313; PMCID: PMC3673908.
  • 37.Ariji Y, Fukuda M, Kise Y, Nozawa M, Yanashita Y, Fujita H, Katsumata A, Ariji E. 2019.
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
Surgery, Oral Medicine, Oral Pathology and Oral Radiology 127(5):458–463.
  • 38.Scrobotă I, Băciuț G, Filip AG, Todor B, Blaga F, Băciuț MF. Application of fuzzy logic in oral cancer risk assessment. Iran J Public Health. 2017;46(5):612–619.
  • 39.Parewe, A. M. A. K., Mahmudy, W. F., Ramdhani, F., & Anggodo, Y. P. (2018). Dental disease detection using hybrid fuzzy logic and evolution strategies. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-8), 27-33.
  • 40.Hiraiwa T, Ariji Y, Fukuda M, Kise Y, Nakata K, Katsumata A, Fujita H, Ariji E. 2019. A
deep-learning artificial intelligence system for assessment of root morphology of the mandibular
first molar on panoramic radiography. Dentomaxillofacial Radiology 48(3):20180218.
  • 41.Saghiri MA, Garcia-Godoy F, Gutmann JL, Lotfi M, Asgar K. The reliability of artificial neural network in locating minor apical foramen: A cadaver study. J Endod. 2012;38(8):1130–1134. doi:10.1016/j.joen. 2012.05.004.
  • 42.Senirkentli, G. B., Sen, S., Farsak, O. and Bostanci, E., “A Neural Expert System Based Dental Trauma Diagnosis Application”, TIPTEKNO 2019, Aydin, Turkey, 2019.
  • 43.Senirkentli, G. B., Ekinci, F., Bostanci, E., Güzel, M. S., Dağli, Ö., Karim, A. M., & Mishra, A. (2021, February). Proton Therapy for Mandibula Plate Phantom. In Healthcare (Vol. 9, No. 2, p. 167). Multidisciplinary Digital Publishing Institute.
  • 44.Goh, W. P., Tao, X., Zhang, J., & Yong, J. (2016). Decision support systems for adoption in dental clinics: a survey. Knowledge-Based Systems, 104, 195-206.
  • 45.Currie G, Hawk KE, Rohren EM. 2020. Ethical principles for the application of artificial intelligence (AI) in nuclear medicine. Eur J Nucl Med Mol Imaging. 47(4):748–752.
  • 46.Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., ... & Lee, S. I. (2020). From local explanations to global understanding with explainable AI for trees. Nature machine intelligence, 2(1), 56-67.
  • 47.Zlotogorski-Hurvitz, A., Dekel, B. Z., Malonek, D., Yahalom, R., & Vered, M. (2019). FTIR-based spectrum of salivary exosomes coupled with computational-aided discriminating analysis in the diagnosis of oral cancer. Journal of cancer research and clinical oncology, 145(3), 685-694.
  • 48.Recht M, Bryan RN. Artificial Intelligence: Threat or Boon to Radiologists? J Am Coll Radiol. 2017 Nov;14(11):1476-1480. doi:10.1016/j.jacr.2017.07.007. Epub 2017 Aug 19. PMID: 28826960.
There are 48 citations in total.

Details

Primary Language English
Subjects Dentistry
Journal Section Review
Authors

Güler Burcu Senirkentli 0000-0003-4918-5504

Gazi Erkan Bostancı 0000-0001-8547-7569

Mehmet Serdar Güzel 0000-0002-3408-0083

Metehan Unal 0000-0002-7545-2445

Publication Date December 26, 2022
Submission Date December 3, 2021
Published in Issue Year 2022 Volume: 9 Issue: 3

Cite

Vancouver Senirkentli GB, Bostancı GE, Güzel MS, Unal M. Machine Learning Applications in Dentistry. Selcuk Dent J. 2022;9(3):977-83.

Selcuk Dental Journal is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY NC).