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DENTOMAKSİLLOFASİYAL RADYOLOJİDE YAPAY ZEKA UYGULAMALARININ ROLÜ: BÖLÜM 2

Yıl 2022, Cilt: 9 Sayı: 2, 721 - 728, 24.08.2022
https://doi.org/10.15311/selcukdentj.855538

Öz

Dijital teknoloji alanındaki gelişmeler; gerek medikal gerekse dental alanda tanı ve tedavi planlamasında yararlanılacak yapay zeka (YZ) uygulamalarına hız vermiştir. YZ, makinelerin insan beyninin çalışmasını taklit ederek karar verme ve tahmin etme gibi çözülmesi zor olan problemlerin çözümüne imkân tanıyan bir alandır. Medikal görüntüleme; yapay zekânın bir alt dalı olan makine öğrenmesi yöntemlerinin en popüler olduğu alanlar içerisinde yer almaktadır. Günümüz medikal araştırma alanlarının başında gelen yapay zeka uygulamaları, radyoloji ve dişhekimliği alanlarında tanı ve tedavi basamaklarının daha düşük maliyet ve daha yüksek doğrulukla gerçekleşmesini sağlamıştır. Bu derlemenin amacı; yapay zekâ uygulamaları içerisinde yer alan farklı ağ mimarileri ve öğrenme algoritmalarının dental disiplinlerdeki mevcut ve potansiyel kullanım alanlarını irdelemektir.

Kaynakça

  • 1. Hung K, Yeung AWK, Tanaka R, Bornstein MM. Current applications, opportunities, and limitations of aı for 3D imaging in dental research and practice. Int J Environ Res Public Health 2020;17:4424.
  • 2. Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. Dentomaxillofac Radiol 2020;49:20190107.
  • 3. Lin PL, Huang PW, Huang PY, Hsu HC. Alveolar bone-loss area localization in periodontitis radiographs based on threshold segmentation with a hybrid feature fused of intensity and the H-value of fractional Brownian motion model. Comput Methods Programs Biomed 2015;121:117–26.
  • 4. Lin PL, Huang PY, Huang PW. Automatic methods for alveolar bone loss degree measurement in periodontitis periapical radiographs. Comput Methods Programs Biomed 2017; 148:1–11.
  • 5. Lee JH, Kim DH, Jeong SN, Choi SH. Diagnosis and prediction of periodontally compromised teeth using a deep learningbased convolutional neural network algorithm. J Periodontal Implant Sci 2018;48:114–23.
  • 6. Imangaliyev S, van der Veen V, Volgenant C, Loos B, Keijser B, Crielaard W, et al. Classification of quantitative light-ınduced fluorescence ımages using convolutional neural network. Arxiv 2017;1705:09193.
  • 7. Rudd K, Bertoncini C, Hinders M. Simulations of ultrasonographic periodontal probe using the finite integration technique. Open Acoust J 2009;2:1-19.
  • 8. Luciano C, Banerjee P, DeFanti T. Haptics‐based virtual reality periodontal training simulator. Virtual Reality 2009;13:69.
  • 9. Firestone AR, Sema D, Heaven TJ, Weems RA. The effect of a knowledge-based, image analysis and clinical decision support system on observer performance in the diagnosis of approximal caries from radiographic images. Caries Res 1998;32:127–34.
  • 10. Gakenheimer DC. The efficacy of a computerized caries detector in intraoral digital radiography. J Am Dent Assoc 2002;133:883– 90.
  • 11. Wenzel A, Hintze H, Kold LM, Kold S. Accuracy of computerautomated caries detection in digital radiographs compared with human observers. Eur J Oral Sci 2002;110:199–203.
  • 12. Schwendicke F, Elhennawy K, Paris S, Friebertshäuser P, Krois J. Deep learning for caries lesion detection in near-infrared light transillumination images: A pilot study. J Dent 2020;92:103260.
  • 13. Devito KL, de Souza Barbosa F, Felippe Filho WN. An artificial multilayer perceptron neural network for diagnosis of proximal dental caries. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2008;106:879-84.
  • 14. Prados-Privado M, García Villalón J, Martínez-Martínez CH, Ivorra C, Prados-Frutos JC. Dental caries diagnosis and detection using neural networks: a systematic review. J Clin Med 2020;9:E3579.
  • 15. Sornam M, Prabhakaran M. A new linear adaptive swarm intelligence approach using back propagation neural network for dental caries classification. In Proceedings of the IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), Chennai, India, 21–22 September 2017; pp. 2698–2703.
  • 16. Singh P, Sehgal P. Automated caries detection based on Radon transformation and DCT. In Proceedings of the 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi, India, 3–5 July 2017; pp. 1–6.
  • 17. Srivastavan MM, Kumar P, Pradhan L, Varadarajan S. Detection of tooth caries in bitewing radiographs using deep learning. In Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, USA, 4–9 December 2017; p. 4.
  • 18. 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;77:106-111.
  • 19. Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi JG, Gaudin R, et al. Detecting caries lesions of different radiographic extension on bitewings using deep learning. J Dent 2020;100:103425.
  • 20. Geetha V, Aprameya KS, Hinduja DM. Dental caries diagnosis in digital radiographs using back-propagation neural network. Health Inf Sci Syst 2020;8:8.
  • 21. 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;98:1227-1233.
  • 22. Liu JK, Chen YT, Cheng KS. Accuracy of computerized automatic identification of cephalometric landmarks. Am J Orthod Dentofacial Orthop 2000;118:35–40.
  • 23. Hutton TJ, Cunningham S, Hammond P. An evaluation of active shape models for the automatic identification of cephalometric landmarks. Eur J Orthod 2000;22:499–508.
  • 24. Grau V, Alcañiz M, Juan MC, Monserrat C, Knoll C. Automatic localization of cephalometric landmarks. J Biomed Inform 200; 34:146–56.
  • 25. Rueda S, Alcañiz M. An approach for the automatic cephalometric landmark detection using mathematical morphology and active appearance models. Med Image Comput Comput Assist Interv 2006;9:159–66.
  • 26. 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;2009:1–12.
  • 27. Vucinić P, Trpovski Z, Sćepan I. Automatic landmarking of cephalograms using active appearance models. Eur J Orthod 2010;32:233–41.
  • 28. Cheng E, Chen J, Yang J, Deng H, Wu Y, Megalooikonomou V, et al. Automatic dent-landmark detection in 3-D CBCT dental volumes. Conf Proc IEEE Eng Med Biol Soc 2011; 2011:6204–7.
  • 29. Shahidi S, Oshagh M, Gozin F, Salehi P, Danaei SM. Accuracy of computerized automatic identification of cephalometric landmarks by a designed software. Dentomaxillofac Radiol 2013;42:20110187.
  • 30. Lindner C, Wang CW, Huang CT, Li CH, Chang SW, Cootes TF. Fully automatic system for accurate localisation and analysis of cephalometric landmarks in lateral cephalograms. Sci Rep 2016;20:33581.
  • 31. Arık Sercan Ö, Ibragimov B, Xing L. Fully automated quantitative cephalometry using convolutional neural networks. J Med Imaging 2017;4:014501.
  • 32. Shahidi S, Bahrampour E, Soltanimehr E, Zamani A, Oshagh M, Moattari M, et al. The accuracy of a designed software for automated localization of craniofacial landmarks on CBCT images. BMC Med Imaging 2014;14:32.
  • 33. Gupta A, Kharbanda OP, Sardana V, Balachandran R, Sardana HK. A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images. Int J Comput Assist Radiol Surg 2015;10:1737–52.
  • 34. Gupta A, Kharbanda OP, Sardana V, Balachandran R, Sardana HK. Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm. Int J Comput Assist Radiol Surg 2016;11:1297–309.
  • 35. Codari M, Caffini M, Tartaglia GM, Sforza C, Baselli G. Computer-aided cephalometric landmark annotation for CBCT data. Int J Comput Assist Radiol Surg 2017;12:113–21.
  • 36. Montúfar J, Romero M, Scougall-Vilchis RJ, Scougall V RJ. Hybrid approach for automatic cephalometric landmark annotation on cone-beam computed tomography volumes. Am J Orthod Dentofacial Orthop 2018;154:140–50.
  • 37. Neelapu BC, Kharbanda OP, Sardana V, Gupta A, Vasamsetti S, Balachandran R, et al. Automatic localization of three-dimensional cephalometric landmarks on CBCT images by extracting symmetry features of the skull. Dentomaxillofac Radiol 2018;47:20170054.
  • 38. Leonardi R, Giordano D, Maiorana F, Spampinato C. Automatic cephalometric analysis. Angle Orthod 2008;78:145–51.
  • 39. Scarfe WC, Farman AG. What is cone-beam CT and how does it work? Dent Clin North Am 2008;52:707-30.
  • 40. Leite AF, Vasconcelos KF, Willems H, Jacobs R. Radiomics and Machine Learning in Oral Healthcare. Proteomics Clin Appl. 2020;14:e1900040.
  • 41. Allareddy V, Rengasamy Venugopalan S, Nalliah RP, Caplin JL, Lee MK, et al. Orthodontics in the era of big data analytics. Orthod Craniofac Res 2019;;22:8-13.
  • 42. 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.
  • 43. 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;80:24-29.
  • 44. Tuzoff DV, Tuzova LN, Bornstein MM, Krasnov AS, Kharchenko MA, Nikolenko SI, Sveshnikov MM, Bednenko GB. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofac Radiol. 2019;48:20180051.
  • 45. Nagi R, Aravinda K, Rakesh N, Jain S, Kaur N, Mann AK. Digitization in forensic odontology: A paradigm shift in forensic investigations. J Forensic Dent Sci. 2019;11:5-10.
  • 46. De Tobel J, Phlypo I, Fieuws S, Politis C, Verstraete KL, Thevissen PW. Forensic age estimation based on development of third molars: a staging technique for magnetic resonance imaging. J Forensic Odontostomatol 2017;35:117-140.
  • 47. De Tobel J, Parmentier GIL, Phlypo I, Descamps B, Neyt S, Van De Velde WL, et al. Magnetic resonance imaging of third molars in forensic age estimation: comparison of the Ghent and Graz protocols focusing on apical closure. Int J Legal Med 2019;133:583-592.
  • 48. 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.
  • 49. 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.
  • 50. Yeung AWK, Jacobs R, Bornstein MM. Novel low-dose protocols using cone beam computed tomography in dental medicine: a review focusing on indications, limitations, and future possibilities. Clin Oral Investig 2019;23:2573-81.
  • 51. Park J, Hwang D, Kim KY, Kang SK, Kim YK, Lee JS. Computed tomography super-resolution using deep convolutional neural network. Phys Med Biol 2018;63:145011.
  • 52. Minnema J, van 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.
  • 53. Abdolali F, Zoroofi RA, Otake Y, Sato Y. Automatic segmentation of maxillofacial cysts in cone beam CT images. Comput Biol Med 2016;72:108-19.
  • 54. Abdolali F, Zoroofi RA, Otake Y, Sato Y. Automated classification of maxillofacial cysts in cone beam CT images using contourlet transformation and Spherical Harmonics. Comput Methods Programs Biomed 2017;139:197-207.
  • 55. Chen YW, Stanley K, Att W. Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int 2020;51:248-57.

ROLE OF ARTIFICIAL INTELLIGENCE APPLICATIONS IN DENTOMAXILLOFACIAL RADIOLOGY: PART 2

Yıl 2022, Cilt: 9 Sayı: 2, 721 - 728, 24.08.2022
https://doi.org/10.15311/selcukdentj.855538

Öz

Advances in digital technology accelerated the artificial intelligence (AI) applications that can be used in diagnosis and treatment planning in both medical and dental fields. AI is a field that allows machines to solve difficult problems such as decision making and prediction by imitating the work of the human brain. Medical imaging is among the most popular areas of machine learning methods which is a sub-branch of artificial intelligence. Artificial intelligence applications, which are one of the leading medical research areas of today, have enabled diagnosis and treatment steps in the fields of radiology and dentistry to be performed with lower costs and higher accuracy. The aim of this review is to examine the current and potential areas of different network architectures and learning algorithms in dental disciplines within artificial intelligence applications

Kaynakça

  • 1. Hung K, Yeung AWK, Tanaka R, Bornstein MM. Current applications, opportunities, and limitations of aı for 3D imaging in dental research and practice. Int J Environ Res Public Health 2020;17:4424.
  • 2. Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. Dentomaxillofac Radiol 2020;49:20190107.
  • 3. Lin PL, Huang PW, Huang PY, Hsu HC. Alveolar bone-loss area localization in periodontitis radiographs based on threshold segmentation with a hybrid feature fused of intensity and the H-value of fractional Brownian motion model. Comput Methods Programs Biomed 2015;121:117–26.
  • 4. Lin PL, Huang PY, Huang PW. Automatic methods for alveolar bone loss degree measurement in periodontitis periapical radiographs. Comput Methods Programs Biomed 2017; 148:1–11.
  • 5. Lee JH, Kim DH, Jeong SN, Choi SH. Diagnosis and prediction of periodontally compromised teeth using a deep learningbased convolutional neural network algorithm. J Periodontal Implant Sci 2018;48:114–23.
  • 6. Imangaliyev S, van der Veen V, Volgenant C, Loos B, Keijser B, Crielaard W, et al. Classification of quantitative light-ınduced fluorescence ımages using convolutional neural network. Arxiv 2017;1705:09193.
  • 7. Rudd K, Bertoncini C, Hinders M. Simulations of ultrasonographic periodontal probe using the finite integration technique. Open Acoust J 2009;2:1-19.
  • 8. Luciano C, Banerjee P, DeFanti T. Haptics‐based virtual reality periodontal training simulator. Virtual Reality 2009;13:69.
  • 9. Firestone AR, Sema D, Heaven TJ, Weems RA. The effect of a knowledge-based, image analysis and clinical decision support system on observer performance in the diagnosis of approximal caries from radiographic images. Caries Res 1998;32:127–34.
  • 10. Gakenheimer DC. The efficacy of a computerized caries detector in intraoral digital radiography. J Am Dent Assoc 2002;133:883– 90.
  • 11. Wenzel A, Hintze H, Kold LM, Kold S. Accuracy of computerautomated caries detection in digital radiographs compared with human observers. Eur J Oral Sci 2002;110:199–203.
  • 12. Schwendicke F, Elhennawy K, Paris S, Friebertshäuser P, Krois J. Deep learning for caries lesion detection in near-infrared light transillumination images: A pilot study. J Dent 2020;92:103260.
  • 13. Devito KL, de Souza Barbosa F, Felippe Filho WN. An artificial multilayer perceptron neural network for diagnosis of proximal dental caries. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2008;106:879-84.
  • 14. Prados-Privado M, García Villalón J, Martínez-Martínez CH, Ivorra C, Prados-Frutos JC. Dental caries diagnosis and detection using neural networks: a systematic review. J Clin Med 2020;9:E3579.
  • 15. Sornam M, Prabhakaran M. A new linear adaptive swarm intelligence approach using back propagation neural network for dental caries classification. In Proceedings of the IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), Chennai, India, 21–22 September 2017; pp. 2698–2703.
  • 16. Singh P, Sehgal P. Automated caries detection based on Radon transformation and DCT. In Proceedings of the 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi, India, 3–5 July 2017; pp. 1–6.
  • 17. Srivastavan MM, Kumar P, Pradhan L, Varadarajan S. Detection of tooth caries in bitewing radiographs using deep learning. In Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, USA, 4–9 December 2017; p. 4.
  • 18. 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;77:106-111.
  • 19. Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi JG, Gaudin R, et al. Detecting caries lesions of different radiographic extension on bitewings using deep learning. J Dent 2020;100:103425.
  • 20. Geetha V, Aprameya KS, Hinduja DM. Dental caries diagnosis in digital radiographs using back-propagation neural network. Health Inf Sci Syst 2020;8:8.
  • 21. 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;98:1227-1233.
  • 22. Liu JK, Chen YT, Cheng KS. Accuracy of computerized automatic identification of cephalometric landmarks. Am J Orthod Dentofacial Orthop 2000;118:35–40.
  • 23. Hutton TJ, Cunningham S, Hammond P. An evaluation of active shape models for the automatic identification of cephalometric landmarks. Eur J Orthod 2000;22:499–508.
  • 24. Grau V, Alcañiz M, Juan MC, Monserrat C, Knoll C. Automatic localization of cephalometric landmarks. J Biomed Inform 200; 34:146–56.
  • 25. Rueda S, Alcañiz M. An approach for the automatic cephalometric landmark detection using mathematical morphology and active appearance models. Med Image Comput Comput Assist Interv 2006;9:159–66.
  • 26. 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;2009:1–12.
  • 27. Vucinić P, Trpovski Z, Sćepan I. Automatic landmarking of cephalograms using active appearance models. Eur J Orthod 2010;32:233–41.
  • 28. Cheng E, Chen J, Yang J, Deng H, Wu Y, Megalooikonomou V, et al. Automatic dent-landmark detection in 3-D CBCT dental volumes. Conf Proc IEEE Eng Med Biol Soc 2011; 2011:6204–7.
  • 29. Shahidi S, Oshagh M, Gozin F, Salehi P, Danaei SM. Accuracy of computerized automatic identification of cephalometric landmarks by a designed software. Dentomaxillofac Radiol 2013;42:20110187.
  • 30. Lindner C, Wang CW, Huang CT, Li CH, Chang SW, Cootes TF. Fully automatic system for accurate localisation and analysis of cephalometric landmarks in lateral cephalograms. Sci Rep 2016;20:33581.
  • 31. Arık Sercan Ö, Ibragimov B, Xing L. Fully automated quantitative cephalometry using convolutional neural networks. J Med Imaging 2017;4:014501.
  • 32. Shahidi S, Bahrampour E, Soltanimehr E, Zamani A, Oshagh M, Moattari M, et al. The accuracy of a designed software for automated localization of craniofacial landmarks on CBCT images. BMC Med Imaging 2014;14:32.
  • 33. Gupta A, Kharbanda OP, Sardana V, Balachandran R, Sardana HK. A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images. Int J Comput Assist Radiol Surg 2015;10:1737–52.
  • 34. Gupta A, Kharbanda OP, Sardana V, Balachandran R, Sardana HK. Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm. Int J Comput Assist Radiol Surg 2016;11:1297–309.
  • 35. Codari M, Caffini M, Tartaglia GM, Sforza C, Baselli G. Computer-aided cephalometric landmark annotation for CBCT data. Int J Comput Assist Radiol Surg 2017;12:113–21.
  • 36. Montúfar J, Romero M, Scougall-Vilchis RJ, Scougall V RJ. Hybrid approach for automatic cephalometric landmark annotation on cone-beam computed tomography volumes. Am J Orthod Dentofacial Orthop 2018;154:140–50.
  • 37. Neelapu BC, Kharbanda OP, Sardana V, Gupta A, Vasamsetti S, Balachandran R, et al. Automatic localization of three-dimensional cephalometric landmarks on CBCT images by extracting symmetry features of the skull. Dentomaxillofac Radiol 2018;47:20170054.
  • 38. Leonardi R, Giordano D, Maiorana F, Spampinato C. Automatic cephalometric analysis. Angle Orthod 2008;78:145–51.
  • 39. Scarfe WC, Farman AG. What is cone-beam CT and how does it work? Dent Clin North Am 2008;52:707-30.
  • 40. Leite AF, Vasconcelos KF, Willems H, Jacobs R. Radiomics and Machine Learning in Oral Healthcare. Proteomics Clin Appl. 2020;14:e1900040.
  • 41. Allareddy V, Rengasamy Venugopalan S, Nalliah RP, Caplin JL, Lee MK, et al. Orthodontics in the era of big data analytics. Orthod Craniofac Res 2019;;22:8-13.
  • 42. 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.
  • 43. 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;80:24-29.
  • 44. Tuzoff DV, Tuzova LN, Bornstein MM, Krasnov AS, Kharchenko MA, Nikolenko SI, Sveshnikov MM, Bednenko GB. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofac Radiol. 2019;48:20180051.
  • 45. Nagi R, Aravinda K, Rakesh N, Jain S, Kaur N, Mann AK. Digitization in forensic odontology: A paradigm shift in forensic investigations. J Forensic Dent Sci. 2019;11:5-10.
  • 46. De Tobel J, Phlypo I, Fieuws S, Politis C, Verstraete KL, Thevissen PW. Forensic age estimation based on development of third molars: a staging technique for magnetic resonance imaging. J Forensic Odontostomatol 2017;35:117-140.
  • 47. De Tobel J, Parmentier GIL, Phlypo I, Descamps B, Neyt S, Van De Velde WL, et al. Magnetic resonance imaging of third molars in forensic age estimation: comparison of the Ghent and Graz protocols focusing on apical closure. Int J Legal Med 2019;133:583-592.
  • 48. 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.
  • 49. 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.
  • 50. Yeung AWK, Jacobs R, Bornstein MM. Novel low-dose protocols using cone beam computed tomography in dental medicine: a review focusing on indications, limitations, and future possibilities. Clin Oral Investig 2019;23:2573-81.
  • 51. Park J, Hwang D, Kim KY, Kang SK, Kim YK, Lee JS. Computed tomography super-resolution using deep convolutional neural network. Phys Med Biol 2018;63:145011.
  • 52. Minnema J, van 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.
  • 53. Abdolali F, Zoroofi RA, Otake Y, Sato Y. Automatic segmentation of maxillofacial cysts in cone beam CT images. Comput Biol Med 2016;72:108-19.
  • 54. Abdolali F, Zoroofi RA, Otake Y, Sato Y. Automated classification of maxillofacial cysts in cone beam CT images using contourlet transformation and Spherical Harmonics. Comput Methods Programs Biomed 2017;139:197-207.
  • 55. Chen YW, Stanley K, Att W. Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int 2020;51:248-57.
Toplam 55 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Diş Hekimliği
Bölüm Derleme
Yazarlar

Elif Şener 0000-0003-1402-9392

Güniz Baksi Şen 0000-0001-5720-2947

Yayımlanma Tarihi 24 Ağustos 2022
Gönderilme Tarihi 6 Ocak 2021
Yayımlandığı Sayı Yıl 2022 Cilt: 9 Sayı: 2

Kaynak Göster

Vancouver Şener E, Baksi Şen G. DENTOMAKSİLLOFASİYAL RADYOLOJİDE YAPAY ZEKA UYGULAMALARININ ROLÜ: BÖLÜM 2. Selcuk Dent J. 2022;9(2):721-8.