Applications of Artificial Intelligence in Dentomaxillofacial Diagnostics

Authors

Keywords:

artificial intelligence, diagnostic imaging, radiology, x-ray computed tomography, deep learning.

Abstract

Introduction: The introduction of artificial intelligence-driven applications is revolutionizing dentomaxillofacial imaging.

Objectives: To describe the current status of artificial intelligence applications in dentomaxillofacial diagnostics; to assess their impact; and to identify future directions for research and implementation.

Methods: A narrative review was performed, using systematic searches in databases such as PubMed, Google Scholar, IEEE Xplore, among others; the study focused on articles published from 2010 to the present. Researches applying artificial intelligence technologies in dentomaxillofacial diagnosis were included; their quality and relevance were evaluated using the established tools.

Results: Artificial intelligence, especially deep learning, has shown significant improvements in image segmentation, disease detection and treatment planning in dentomaxillofacial imaging. Artificial intelligence techniques have enabled automation of image analysis tasks, improved efficiency and diagnostic accuracy.

Conclusions: Artificial intelligence has significant potential to revolutionize dentomaxillofacial imaging, as it offers improvements in diagnostic accuracy, efficiency in image interpretation, and treatment planning. Further research is needed to overcome technical, ethical and privacy challenges and to validate the clinical applicability of these technologies.

Downloads

Download data is not yet available.

Author Biography

Iván Claudio Suazo Galdames, Universidad Autónoma de Chile. Santiago de Chile, Chile.

Académico y Directivo con experiencia en gestión y dirección universitaria. Líder y articulador de equipos de docencia y de investigación en universidades chilenas y extranjeras. Ha sido par evaluador Institucional para las áreas de Pregrado, postgrado e Investigación. Con un acabado conocimiento del Sistema Universitario Chileno y de los modelos de desarrollo de investigación en América Latina, España y Portugal, ha sido asesor de Universidades en Chile y el extranjero en materia de Investigación y Postgrado. Ha liderado proyectos mayores de desarrollo institucional (MECESUP, INES). Doctor en Ciencias (PhD.), Profesor Titular, con reconocimiento nacional e internacional en docencia e investigación en ciencias biomédicas. Elevada productividad científica, con más de 100 publicaciones en revistas internacionales y 4 libros en su especialidad (https://orcid.org/0000-0002-16891632). Ha sido director de departamentos Universitarios en Facultades de Medicina y de Ciencias de la Salud y director de programas de Doctorado y Magister. Ha dictado conferencias en Chile, Argentina, Perú, Brasil, Paraguay, Uruguay, Ecuador, México, Colombia, USA, Canadá, España, Alemania, UK, Portugal y ha sido profesor visitante en Universidades de Brasil y Portugal. Creador del Centro de Comunicación de las Ciencias @ciencias_chile, Editorial “Ediciones Universidad Autónoma de Chile”, la Escuela Internacional de Postgrados (ESIP) y la Oficina de transferencia y Licenciamiento OTL UA. Con una amplia red de contactos internacionales, actualmente es miembro de Agencias evaluadoras en América latina (EQUAA) y de diferentes agencias del Espacio Europeo de Educación Superior, investigador asociado senior de la Universidad de Oviedo, España y Editor in Chief de las Revistas International Journal o Medical and Surgical Sciences y European Journal of Health Research.

References

Jain S, Choudhary K, Nagi R, Shukla S, Kaur N, Grover D. New evolution of cone-beam computed tomography in dentistry: combining digital technologies. Imaging Science in Dentistry. 2019;49(3):179. DOI: https://doi.org/10.5624/isd.2019.49.3.179

Patel S, Dawood A, Ford T, Whaites E. The potential applications of cone beam computed tomography in the management of endodontic problems. Int Endod J. 2007;40(10):818-30. DOI: https://doi.org/10.1111/j.1365-2591.2007.01299.x

Oenning A, Jacobs R, Pauwels R, Stratis A, Hedeşiu M, Salmon B. Cone-beam CT in paediatric dentistry: dimitra project position statement. Pediatr Radiol. 2017 [acceso 28/11/2023];48(3):308-16. Disponible en: https://link.springer.com/article/10.1007/s00247-017-4012-9

Hung K, Yeung A, Tanaka R, Bornstein M. Current applications, opportunities, and limitations of ai for 3D imaging in dental research and practice. International Journal of Environmental Research and Public Health. 2020;17(12):4424. DOI: https://doi.org/10.3390/ijerph17124424

Kumar M, Madi M, Pentapati K, Vineetha R. Reliability of linear and curvilinear measurements on cone-beam computed tomography images for the evaluation of implant sites and jaw pathologies. Pesquisa Brasileira Em Odontopediatria e Clínica Integrada. 2021;21. DOI: https://doi.org/10.1590/pboci.2021.023

Pauwels R, Faruangsaeng T, Charoenkarn T, Ngonphloy N, Panmekiate S. Effect of exposure parameters and voxel size on bone structure analysis in CBCT. Dentomaxillofac Radiol. 2015;44(8):20150078. DOI: https://doi.org/10.1259/dmfr.20150078

Cakli H, Cingi C, Ünüvar Y, Oghan F, Özer T, Kaya E. Use of cone beam computed tomography in otolaryngologic treatments. European Archives of Oto-Rhino-Laryngology. 2011;269(3):711-20. DOI: https://doi.org/10.1007/s00405-011-1781-x

Nazerani T, Kalmar P, Aigner R. Emerging role of nuclear medicine in oral and maxillofacial surgery. DOI: https://doi.org/10.5772/intechopen.92278

Hosny A, Parmar C, Quackenbush J, Schwartz L, Aerts H. Artificial intelligence in radiology. Nature Reviews Cancer. 2018;18(8):500-10. DOI: https://doi.org/10.1038/s41568-018-0016-5

Leite A, Vasconcelos K, Willems H, Jacobs R. Radiomics and machine learning in oral healthcare. Proteomics-Clinical Applications. 2020;14(3). DOI: https://doi.org/10.1002/prca.201900040

Leite A, Gerven A, Willems H, Beznik T, Lahoud P, Gaêta‐Araujo H, et al. Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs. Clinical Oral Investigations. 2020;25(4):2257-67. DOI: https://doi.org/10.1007/s00784-020-03544-6

Jaskari J, Sahlsten J, Järnstedt J, Mehtonen H, Karhu K, Sundqvist O, et al. Deep learning method for mandibular canal segmentation in dental cone beam computed tomography volumes. Scientific Reports. 2020;10(1). DOI: https://doi.org/10.1038/s41598-020-62321-3

Amasya H, Alkhader M, Serindere G, Futyma-Gąbka K, Belgin C, Gusarev M, et al. Evaluation of a decision support system developed with deep learning approach for detecting dental caries with cone-beam computed tomography imaging. 2023. DOI: https://doi.org/10.21203/rs.3.rs-3108030/v1

Gokdeniz S, Kamburoğlu K. Artificial intelligence in dentomaxillofacial radiology. World Journal of Radiology. 2022;14(3):55-9. DOI: https://doi.org/10.4329/wjr.v14.i3.55

Park W, Park J. History and application of artificial neural networks in dentistry. Eur J Dent. 2018;12(04):594-601. DOI: https://doi.org/10.4103/ejd.ejd_325_18

Suomalainen A, Esmaeili E, Robinson A. Dentomaxillofacial imaging with panoramic views and cone beam CT. Insights Imaging. 2015;6(1):1-16. DOI: https://doi.org/10.1007/s13244-014-0379-4

Krishnamoorthy B, Mamatha N, Kumar V. TMJ imaging by CBCT: current scenario. Annals of Maxillofacial Surgery. 2013;3(1):80. DOI: https://doi.org/10.4103/2231-0746.110069

Nagi R, Konidena A, Rakesh N, Gupta R, Pal A, Mann A. Clinical applications and performance of intelligent systems in dental and maxillofacial radiology: a review. Imaging Science in Dentistry. 2020;50(2):81. DOI: https://doi.org/10.5624/isd.2020.50.2.81

Xiao Y, Liang Q, Zhang L, He X, Lv L, Endian S, et al. Construction of a new automatic grading system for jaw bone mineral density level based on deep learning using cone beam computed tomography. Sci Rep. 2022;12(1). DOI: https://doi.org/10.1038/s41598-022-16074-w

Belmans N, Gilles L, Virág P, Hedeșiu M, Salmon B, Baatout S, et al. Method validation to assess in vivo cellular and subcellular changes in buccal mucosa cells and saliva following cbct examinations. Dentomaxillofacial Radiology. 2019;48(6):20180428. DOI: https://doi.org/10.1259/dmfr.20180428

Fatima M, Pasha M. Survey of machine learning algorithms for disease diagnostic. Journal of Intelligent Learning Systems and Applications. 2017;9(1):1-16. DOI: https://doi.org/10.4236/jilsa.2017.91001

Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O. Deep learning with convolutional neural network in radiology. Jpn J Radiol. 2018;36(4):257-72. DOI: https://doi.org/10.1007/s11604-018-0726-3

Singh C. Medical imaging using deep learning models. Eur J Eng Technol Res. 2021;6(5):156-67. DOI: https://doi.org/10.24018/ejeng.2021.6.5.2491.

Du W, Rao N, Liu D, Jiang H, Luo C, Li Z, et al. Review on the applications of deep learning in the analysis of gastrointestinal endoscopy images. IEEE Access. 2019;7:142053-69. DOI: https://doi.org/10.1109/access.2019.2944676

Tang Y, Qiu J, Gao M. Fuzzy medical computer vision image restoration and visual application. Comput Math Methods Med. 2022;2022:1-10. DOI: https://doi.org/10.1155/2022/6454550

Wijaya N. Capital letter pattern recognition in text to speech by way of perceptron algorithm. Knowl Eng Data Sci. 2017 [acceso 28/11/2023];1(1):26. https://journal2.um.ac.id/index.php/keds/article/view/1289

Szabó B, Dobai A, Dobó-Nagy C. Cone-beam computed tomography in dentomaxillofacial radiology. [acceso 28/11/2023]. Disponible en: https://www.intechopen.com/chapters/70883

Sukovic P. Cone beam computed tomography in craniofacial imaging. Orthod Craniofac Res. 2003;6(s1):31-6. DOI: https://doi.org/10.1034/j.1600-0544.2003.259.x

Bansal S. Determining disease using machine learning algorithm in medical image processing: a gentle review. Biomedical Statistics and Informatics. 2021;6(4):84. DOI: https://doi.org/10.11648/j.bsi.20210604.13

Hwang J, Jung Y, Cho B, Heo M. An overview of deep learning in the field of dentistry. Imaging Science in Dentistry. 2019;49(1):1. DOI: https://doi.org/10.5624/isd.2019.49.1.1

Hatvani J, Horváth A, Michetti J, Basarab A, Kouamé D, Gyöngy M. Deep learning-based super-resolution applied to dental computed tomography. IEEE Transactions on Radiation and Plasma Medical Sciences. 2019;3(2):120-8. DOI: https://doi.org/10.1109/trpms.2018.2827239

Farook T, Jamayet N, Abdullah J, Alam M. Machine learning and intelligent diagnostics in dental and orofacial pain management: a systematic review. Pain Research and Management. 2021;2021:1-9. DOI: https://doi.org/10.1155/2021/6659133

Khanagar S, Al-Ehaideb A, Maganur P, Vishwanathaiah S, Patil S, Baeshen H, et al. Developments, application, and performance of artificial intelligence in dentistry–a systematic review. Journal of Dental Sciences. 2021;16(1):508-22. DOI: https://doi.org/10.1016/j.jds.2020.06.019

Lee K, Kwak H, Oh J, Jha N, Kim Y, Kim W, et al. Automated detection of TMJ osteoarthritis based on artificial intelligence. Journal of Dental Research. 2020;99(12):1363-7. DOI: https://doi.org/10.1177/0022034520936950

Kong Z, Xiong F, Zhang C, Fu Z, Zhang M, Weng J, et al. Automated maxillofacial segmentation in panoramic dental x-ray images using an efficient encoder-decoder network. Ieee Access. 2020;8:207822-33. DOI: https://doi.org/10.1109/access.2020.3037677

Bayrakdar I, Orhan K, Çelik Ö, Bilgir E, Sağlam H, Kaplan F, et al. A u-net approach to apical lesion segmentation on panoramic radiographs. Biomed Research International. 2022;2022:1-7. DOI: https://doi.org/10.1155/2022/7035367

Kanuri N, Abdelkarim A, Rathore S. Trainable weka (waikato environment for knowledge analysis) segmentation tool: machine-learning-enabled segmentation on features of panoramic radiographs. Cureus. 2022. DOI: https://doi.org/10.7759/cureus.21777

Song Y, Jeong H, Kim C, Kim D, Kim J, Kim H, et al. Comparison of detection performance of soft tissue calcifications using artificial intelligence in panoramic radiography. Sci Rep. 2022;12(1). https://doi.org/10.1038/s41598-022-22595-1

Lubner M, Smith A, Sandrasegaran K, Sahani D, Pickhardt P. Ct texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics. 2017;37(5):1483-503. DOI: https://doi.org/10.1148/rg.2017170056

Rizzo S, Botta F, Raimondi S, Origgi D, Fanciullo C, Morganti A, et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp. 2018;2(1). DOI: https://doi.org/10.1186/s41747-018-0068-z

Song J, Yin Y, Wang H, Chang Z, Liu Z, Cui L. A review of original articles published in the emerging field of radiomics. Eur J Radiol. 2020;127:108991. DOI: https://doi.org/10.1016/j.ejrad.2020.108991

Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, et al. Lifex: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018;78(16):4786-9. DOI: https://doi.org/10.1158/0008-5472.can-18-0125

Sollini M, Antunovic L, Chiti A, Kirienko M. Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics. Eur J Nucl Med Mol Imaging. 2019;46(13):2656-72. DOI: https://doi.org/10.1007/s00259-019-04372-x

Downloads

Published

2024-04-10

How to Cite

1.
Suazo Galdames IC. Applications of Artificial Intelligence in Dentomaxillofacial Diagnostics. Rev Cubana Estomatol [Internet]. 2024 Apr. 10 [cited 2025 Feb. 5];61:e4934. Available from: https://revestomatologia.sld.cu/index.php/est/article/view/4934

Issue

Vol. 61 (2024)

Section

Current View

License

Authors retain all rights to their works, which they can reproduce and distribute as long as they cite the primary source of publication.

The Rev Cubana Estomatol is subject to the Creative Commons Attribution-Non-Commercial 4.0 International License (CC BY-NC 4.0) and follows the publication model of SciELO Publishing Schema (SciELO PS) for publication in XML format.

You are free to:

  • Share — copy and redistribute the material in any medium or format.
  • Adapt — remix, transform, and build upon the material.

The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices:

  • You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
  • No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.