Year 2021,
Volume: 38 Issue: 3s, 157 - 162, 09.05.2021
Serdar Akdeniz
,
Muhammet Emir Tosun
References
- Akçam, M. O., & Takada, K., 2002. Fuzzy modelling for selecting headgear types. The European Journal of Orthodontics, 24(1), 99–106.
Auconi, P., Caldarelli, G., Scala, A., Ierardo, G., & Polimeni, A., 2011. A network approach to orthodontic diagnosis. Orthodontics & Craniofacial Research, 14(4), 189–197.
Auconi, P., Scazzocchio, M., Cozza, P., McNamara Jr, J. A., & Franchi, L., 2015. Prediction of Class III treatment outcomes through orthodontic data mining. European Journal of Orthodontics, 37(3), 257–267.
Banumathi, A., Raju, S., & Abhaikumar, V., 2011. Diagnosis of dental deformities in cephalometry images using support vector machine. Journal of Medical Systems, 35(1), 113–119.
Buschang, P. H., Ross, M., Shaw, S. G., Crosby, D., & Campbell, P. M., 2014. Predicted and actual end-of-treatment occlusion produced with aligner therapy. The Angle Orthodontist, 85(5), 723–727. https://doi.org/10.2319/043014-311.1
Faber, J., Faber, C., & Faber, P., 2019. Artificial intelligence in orthodontics. APOS Trends in Orthodontics, 9(4), 201–205.
Faltin, R. M., de Almeida, M. A. A., Kessner, C. A., & Faltin, K. J., 2003. Efficiency, three-dimensional planning and prediction of the orthodontic treatment with the Invisalign® System: case report. R Clín Ortodon Dental Press, 2(2), 61–71.
Grünheid, T., Loh, C., & Larson, B. E., 2017. How accurate is Invisalign in nonextraction cases? Are predicted tooth positions achieved? The Angle Orthodontist, 87(6), 809–815.
Gupta, A., Kharbanda, O. P., Sardana, V., Balachandran, R., & Sardana, H. K., 2015. A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images. International Journal of Computer Assisted Radiology and Surgery, 10(11), 1737–1752.
Hägg, U., & Taranger, J., 1980. Menarche and voice change as indicators of the pubertal growth spurt. Acta Odontologica Scandinavica, 38(3), 179–186.
Hägg, U., & Taranger, J., 1982. Maturation indicators and the pubertal growth spurt. American Journal of Orthodontics, 82(4), 299–309.
Harrar, H., Myers, S., & Ghanem, A. M., 2018. Art or Science? An evidence-based approach to human facial beauty a quantitative analysis towards an informed clinical aesthetic practice. Aesthetic Plastic Surgery, 42(1), 137–146.
Hutton, T. J., Cunningham, S., & Hammond, P., 2000. An evaluation of active shape models for the automatic identification of cephalometric landmarks. The European Journal of Orthodontics, 22(5), 499–508.
Iglovikov, V. I., Rakhlin, A., Kalinin, A. A., & Shvets, A. A., 2018. Paediatric bone age assessment using deep convolutional neural networks. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (pp. 300–308). Springer.
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. https://doi.org/10.1016/j.ajodo.2015.07.030
Kannan, P. V., 2017. Artificial Intelligence–Applications in Healthcare. Asian Hospital & Healthcare Management. Retrieved To30, 5.
Kattadiyil, M. T., Mursic, Z., AlRumaih, H., & Goodacre, C. J., 2014. Intraoral scanning of hard and soft tissues for partial removable dental prosthesis fabrication. The Journal of Prosthetic Dentistry, 112(3), 444–448.
Kesling, H. D., 1945. The philosophy of the tooth positioning appliance. American Journal of Orthodontics and Oral Surgery, 31(6), 297-304.
Khanna, S., 2010. Artificial intelligence: contemporary applications and future compass. International Dental Journal, 60(4), 269–272.
Kim, B.-M., Kang, B.-Y., Kim, H.-G., & Baek, S.-H., 2009. Prognosis prediction for class III malocclusion treatment by feature wrapping method. The Angle Orthodontist, 79(4), 683–691.
Knight, H., & Keith, O., 2005. Ranking facial attractiveness. The European Journal of Orthodontics, 27(4), 340–348.
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(1), 41.
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. Journal of Orofacial Orthopedics= Fortschritte Der Kieferorthopadie: Organ/Official Journal Deutsche Gesellschaft Fur Kieferorthopadie, 81(1), 52.
Larson, D. B., Chen, M. C., Lungren, M. P., Halabi, S. S., Stence, N. V, & Langlotz, C. P., 2018. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology, 287(1), 313–322.
Lee, H., Tajmir, S., Lee, J., Zissen, M., Yeshiwas, B. A., Alkasab, T. K., Choy, G., & Do, S., 2017. Fully automated deep learning system for bone age assessment. Journal of Digital Imaging, 30(4), 427–441.
Lee, K., Ryu, J., Jang, H., 2020. Deep Convolutional Neural Networks Based Analysis of Cephalometric Radiographs for Differential Diagnosis of Orthognathic Surgery Indications. Applied Sciences, 10(6, 2124.
Levy-Mandel, A. D., Tsotsos, J. K., & Venetsanopoulos, A. N., 1985. Knowledge-based landmarking of cephalograms. In Computer Assisted Radiology/Computergestützte Radiologie (pp. 473–478). Springer.
Mario, M. C., Abe, J. M., Ortega, N. R. S., & Del Santo Jr, M., 2010. Paraconsistent artificial neural network as auxiliary in cephalometric diagnosis. Artificial Organs, 34(7), E215–E221.
Miller, R., Dijkman, D., Riolo, M., & Moyers, R., 1971. Graphic computerization of cephalometric data.
Montúfar, J., Romero, M., & Scougall-Vilchis, R. J., 2018a. Automatic 3-dimensional cephalometric landmarking based on active shape models in related projections. American Journal of Orthodontics and Dentofacial Orthopedics, 153(3), 449–458.
Montúfar, J., Romero, M., & Scougall-Vilchis, R. J., 2018b. Hybrid approach for automatic cephalometric landmark annotation on cone-beam computed tomography volumes. American Journal of Orthodontics and Dentofacial Orthopedics, 154(1), 140–150.
Murata, S., Lee, C., Tanikawa, C., & Date, S., 2017. Towards a fully automated diagnostic system for orthodontic treatment in dentistry. 2017 IEEE 13th International Conference on E-Science (e-Science), 1–8.
Nilsson, N. J., & Nilsson, N. J., 1998. Artificial intelligence: a new synthesis. Morgan Kaufmann.
Niño-Sandoval, T. C., Perez, S. V. G., González, F. A., Jaque, R. A., & Infante-Contreras, C., 2016. An automatic method for skeletal patterns classification using craniomaxillary variables on a Colombian population. Forensic Science International, 261, 159-e1.
Niño-Sandoval, T. C., Pérez, S. V. G., González, F. A., Jaque, R. A., & Infante-Contreras, C., 2017. Use of automated learning techniques for predicting mandibular morphology in skeletal class I, II and III. Forensic Science International, 281, 187-e1.
Noroozi, H., 2006. Orthodontic treatment planning software. American Journal of Orthodontics and Dentofacial Orthopedics, 129(6), 834–837.
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. International Journal of Oral and Maxillofacial Surgery, 48(1), 77–83.
Proffit, W. R., Fields, H. W., Larson, B., & Sarver, D. M., 2018. Contemporary Orthodontics. Elsevier Health Sciences.
Spampinato, C., Palazzo, S., Giordano, D., Aldinucci, M., & Leonardi, R., 2017. Deep learning for automated skeletal bone age assessment in X-ray images. Medical Image Analysis, 36, 41–51.
Tanikawa, C., Yagi, M., & Takada, K., 2009. Automated cephalometry: system performance reliability using landmark-dependent criteria. The Angle Orthodontist, 79(6), 1037–1046.
Tanikawa, C., Yamamoto, T., Yagi, M., & Takada, K., 2010. Automatic recognition of anatomic features on cephalograms of preadolescent children. The Angle Orthodontist, 80(5), 812–820.
Tong, W., Nugent, S. T., Jensen, G. M., & Fay, D. F., 1989. An algorithm for locating landmarks on dental X-rays. Images of the Twenty-First Century. Proceedings of the Annual International Engineering in Medicine and Biology Society, 552–554.
Vecsei, B., Joós-Kovács, G., Borbély, J., & Hermann, P., 2017. Comparison of the accuracy of direct and indirect three-dimensional digitizing processes for CAD/CAM systems–an in vitro study. Journal of Prosthodontic Research, 61(2), 177–184.
Wang, X., Cai, B., Cao, Y., Zhou, C., Yang, L., Liu, R., Long, X., Wang, W., Gao, D., & Bao, B., 2016. Objective method for evaluating orthodontic treatment from the lay perspective: An eye-tracking study. American Journal of Orthodontics and Dentofacial Orthopedics, 150(4), 601–610.
Xie, X., Wang, L., & Wang, A., 2010. Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. The Angle Orthodontist, 80(2), 262–266.
Yagi, M., Ohno, H., & Takada, K., 2010. Decision-making system for orthodontic treatment planning based on direct implementation of expertise knowledge. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2894–2897.
Yin, L., Jiang, M., Chen, W., Smales, R. J., Wang, Q., & Tang, L., 2014. Differences in facial profile and dental esthetic perceptions between young adults and orthodontists. American Journal of Orthodontics and Dentofacial Orthopedics, 145(6), 750–756.
Yu, X., Liu, B., Pei, Y., & Xu, T., 2014. Evaluation of facial attractiveness for patients with malocclusion: a machine-learning technique employing Procrustes. The Angle Orthodontist, 84(3), 410–416.
Zarei, A., El-Sharkawi, M., Hairfield, M., & King, G., 2006. An intelligent system for prediction of orthodontic treatment outcome. The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2702–2706.
A review of the use of artificial intelligence in orthodontics
Year 2021,
Volume: 38 Issue: 3s, 157 - 162, 09.05.2021
Serdar Akdeniz
,
Muhammet Emir Tosun
Abstract
The clinical use of artificial intelligence technology in orthodontics has increased significantly in recent years. Artificial intelligence can be utilized in almost every part of orthodontic workflow. It is an important decision making aid as well as a tool for building more efficient treatment mechanics. The use of artificial intelligence reduces costs, speeds up the diagnosis and treatment process and reduces or even eliminates the need for manpower.
This review article evaluates the current literature on artificial intelligence and machine learning in the field of orthodontics. The areas that the artificial intelligence is still lacking were also discussed in detail. Despite its shortcomings, artificial intelligence is considered to have an integral part of orthodontic practice in the near future.
References
- Akçam, M. O., & Takada, K., 2002. Fuzzy modelling for selecting headgear types. The European Journal of Orthodontics, 24(1), 99–106.
Auconi, P., Caldarelli, G., Scala, A., Ierardo, G., & Polimeni, A., 2011. A network approach to orthodontic diagnosis. Orthodontics & Craniofacial Research, 14(4), 189–197.
Auconi, P., Scazzocchio, M., Cozza, P., McNamara Jr, J. A., & Franchi, L., 2015. Prediction of Class III treatment outcomes through orthodontic data mining. European Journal of Orthodontics, 37(3), 257–267.
Banumathi, A., Raju, S., & Abhaikumar, V., 2011. Diagnosis of dental deformities in cephalometry images using support vector machine. Journal of Medical Systems, 35(1), 113–119.
Buschang, P. H., Ross, M., Shaw, S. G., Crosby, D., & Campbell, P. M., 2014. Predicted and actual end-of-treatment occlusion produced with aligner therapy. The Angle Orthodontist, 85(5), 723–727. https://doi.org/10.2319/043014-311.1
Faber, J., Faber, C., & Faber, P., 2019. Artificial intelligence in orthodontics. APOS Trends in Orthodontics, 9(4), 201–205.
Faltin, R. M., de Almeida, M. A. A., Kessner, C. A., & Faltin, K. J., 2003. Efficiency, three-dimensional planning and prediction of the orthodontic treatment with the Invisalign® System: case report. R Clín Ortodon Dental Press, 2(2), 61–71.
Grünheid, T., Loh, C., & Larson, B. E., 2017. How accurate is Invisalign in nonextraction cases? Are predicted tooth positions achieved? The Angle Orthodontist, 87(6), 809–815.
Gupta, A., Kharbanda, O. P., Sardana, V., Balachandran, R., & Sardana, H. K., 2015. A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images. International Journal of Computer Assisted Radiology and Surgery, 10(11), 1737–1752.
Hägg, U., & Taranger, J., 1980. Menarche and voice change as indicators of the pubertal growth spurt. Acta Odontologica Scandinavica, 38(3), 179–186.
Hägg, U., & Taranger, J., 1982. Maturation indicators and the pubertal growth spurt. American Journal of Orthodontics, 82(4), 299–309.
Harrar, H., Myers, S., & Ghanem, A. M., 2018. Art or Science? An evidence-based approach to human facial beauty a quantitative analysis towards an informed clinical aesthetic practice. Aesthetic Plastic Surgery, 42(1), 137–146.
Hutton, T. J., Cunningham, S., & Hammond, P., 2000. An evaluation of active shape models for the automatic identification of cephalometric landmarks. The European Journal of Orthodontics, 22(5), 499–508.
Iglovikov, V. I., Rakhlin, A., Kalinin, A. A., & Shvets, A. A., 2018. Paediatric bone age assessment using deep convolutional neural networks. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (pp. 300–308). Springer.
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. https://doi.org/10.1016/j.ajodo.2015.07.030
Kannan, P. V., 2017. Artificial Intelligence–Applications in Healthcare. Asian Hospital & Healthcare Management. Retrieved To30, 5.
Kattadiyil, M. T., Mursic, Z., AlRumaih, H., & Goodacre, C. J., 2014. Intraoral scanning of hard and soft tissues for partial removable dental prosthesis fabrication. The Journal of Prosthetic Dentistry, 112(3), 444–448.
Kesling, H. D., 1945. The philosophy of the tooth positioning appliance. American Journal of Orthodontics and Oral Surgery, 31(6), 297-304.
Khanna, S., 2010. Artificial intelligence: contemporary applications and future compass. International Dental Journal, 60(4), 269–272.
Kim, B.-M., Kang, B.-Y., Kim, H.-G., & Baek, S.-H., 2009. Prognosis prediction for class III malocclusion treatment by feature wrapping method. The Angle Orthodontist, 79(4), 683–691.
Knight, H., & Keith, O., 2005. Ranking facial attractiveness. The European Journal of Orthodontics, 27(4), 340–348.
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(1), 41.
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. Journal of Orofacial Orthopedics= Fortschritte Der Kieferorthopadie: Organ/Official Journal Deutsche Gesellschaft Fur Kieferorthopadie, 81(1), 52.
Larson, D. B., Chen, M. C., Lungren, M. P., Halabi, S. S., Stence, N. V, & Langlotz, C. P., 2018. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology, 287(1), 313–322.
Lee, H., Tajmir, S., Lee, J., Zissen, M., Yeshiwas, B. A., Alkasab, T. K., Choy, G., & Do, S., 2017. Fully automated deep learning system for bone age assessment. Journal of Digital Imaging, 30(4), 427–441.
Lee, K., Ryu, J., Jang, H., 2020. Deep Convolutional Neural Networks Based Analysis of Cephalometric Radiographs for Differential Diagnosis of Orthognathic Surgery Indications. Applied Sciences, 10(6, 2124.
Levy-Mandel, A. D., Tsotsos, J. K., & Venetsanopoulos, A. N., 1985. Knowledge-based landmarking of cephalograms. In Computer Assisted Radiology/Computergestützte Radiologie (pp. 473–478). Springer.
Mario, M. C., Abe, J. M., Ortega, N. R. S., & Del Santo Jr, M., 2010. Paraconsistent artificial neural network as auxiliary in cephalometric diagnosis. Artificial Organs, 34(7), E215–E221.
Miller, R., Dijkman, D., Riolo, M., & Moyers, R., 1971. Graphic computerization of cephalometric data.
Montúfar, J., Romero, M., & Scougall-Vilchis, R. J., 2018a. Automatic 3-dimensional cephalometric landmarking based on active shape models in related projections. American Journal of Orthodontics and Dentofacial Orthopedics, 153(3), 449–458.
Montúfar, J., Romero, M., & Scougall-Vilchis, R. J., 2018b. Hybrid approach for automatic cephalometric landmark annotation on cone-beam computed tomography volumes. American Journal of Orthodontics and Dentofacial Orthopedics, 154(1), 140–150.
Murata, S., Lee, C., Tanikawa, C., & Date, S., 2017. Towards a fully automated diagnostic system for orthodontic treatment in dentistry. 2017 IEEE 13th International Conference on E-Science (e-Science), 1–8.
Nilsson, N. J., & Nilsson, N. J., 1998. Artificial intelligence: a new synthesis. Morgan Kaufmann.
Niño-Sandoval, T. C., Perez, S. V. G., González, F. A., Jaque, R. A., & Infante-Contreras, C., 2016. An automatic method for skeletal patterns classification using craniomaxillary variables on a Colombian population. Forensic Science International, 261, 159-e1.
Niño-Sandoval, T. C., Pérez, S. V. G., González, F. A., Jaque, R. A., & Infante-Contreras, C., 2017. Use of automated learning techniques for predicting mandibular morphology in skeletal class I, II and III. Forensic Science International, 281, 187-e1.
Noroozi, H., 2006. Orthodontic treatment planning software. American Journal of Orthodontics and Dentofacial Orthopedics, 129(6), 834–837.
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. International Journal of Oral and Maxillofacial Surgery, 48(1), 77–83.
Proffit, W. R., Fields, H. W., Larson, B., & Sarver, D. M., 2018. Contemporary Orthodontics. Elsevier Health Sciences.
Spampinato, C., Palazzo, S., Giordano, D., Aldinucci, M., & Leonardi, R., 2017. Deep learning for automated skeletal bone age assessment in X-ray images. Medical Image Analysis, 36, 41–51.
Tanikawa, C., Yagi, M., & Takada, K., 2009. Automated cephalometry: system performance reliability using landmark-dependent criteria. The Angle Orthodontist, 79(6), 1037–1046.
Tanikawa, C., Yamamoto, T., Yagi, M., & Takada, K., 2010. Automatic recognition of anatomic features on cephalograms of preadolescent children. The Angle Orthodontist, 80(5), 812–820.
Tong, W., Nugent, S. T., Jensen, G. M., & Fay, D. F., 1989. An algorithm for locating landmarks on dental X-rays. Images of the Twenty-First Century. Proceedings of the Annual International Engineering in Medicine and Biology Society, 552–554.
Vecsei, B., Joós-Kovács, G., Borbély, J., & Hermann, P., 2017. Comparison of the accuracy of direct and indirect three-dimensional digitizing processes for CAD/CAM systems–an in vitro study. Journal of Prosthodontic Research, 61(2), 177–184.
Wang, X., Cai, B., Cao, Y., Zhou, C., Yang, L., Liu, R., Long, X., Wang, W., Gao, D., & Bao, B., 2016. Objective method for evaluating orthodontic treatment from the lay perspective: An eye-tracking study. American Journal of Orthodontics and Dentofacial Orthopedics, 150(4), 601–610.
Xie, X., Wang, L., & Wang, A., 2010. Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. The Angle Orthodontist, 80(2), 262–266.
Yagi, M., Ohno, H., & Takada, K., 2010. Decision-making system for orthodontic treatment planning based on direct implementation of expertise knowledge. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2894–2897.
Yin, L., Jiang, M., Chen, W., Smales, R. J., Wang, Q., & Tang, L., 2014. Differences in facial profile and dental esthetic perceptions between young adults and orthodontists. American Journal of Orthodontics and Dentofacial Orthopedics, 145(6), 750–756.
Yu, X., Liu, B., Pei, Y., & Xu, T., 2014. Evaluation of facial attractiveness for patients with malocclusion: a machine-learning technique employing Procrustes. The Angle Orthodontist, 84(3), 410–416.
Zarei, A., El-Sharkawi, M., Hairfield, M., & King, G., 2006. An intelligent system for prediction of orthodontic treatment outcome. The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2702–2706.