Aplicaciones de la inteligencia artificial en el diagnóstico dentomaxilofacial
Resumen
Introducción: La introducción de aplicaciones impulsadas por la inteligencia artificial está revolucionando la imagenología dentomaxilofacial.
Objetivos: Describir el estado actual de las aplicaciones de la inteligencia artificial en el diagnóstico dentomaxilofacial; evaluar su impacto e identificar direcciones futuras para la investigación y la implementación.
Método: Se realizó una revisión narrativa, utilizando búsquedas sistemáticas en bases de datos como PubMed, Google Scholar, IEEE Xplore, entre otras; el estudio se enfocó en artículos publicados desde 2010 hasta la actualidad. Se incluyeron investigaciones que aplican tecnologías de la inteligencia artificial en el diagnóstico dentomaxilofacial; se evaluó su calidad y relevancia mediante las herramientas establecidas.
Resultados: La inteligencia artificial, especialmente el aprendizaje profundo, ha mostrado mejoras significativas en la segmentación de imágenes, la detección de enfermedades y la planificación del tratamiento en imagenología dentomaxilofacial. Las técnicas de inteligencia artificial han permitido la automatización de tareas de análisis de imágenes, mejorado la eficiencia y la precisión diagnóstica.
Conclusiones: La inteligencia artificial posee un potencial significativo para revolucionar la imagenología dentomaxilofacial, pues ofrece mejoras en la precisión diagnóstica, eficiencia en la interpretación de imágenes y en la planificación del tratamiento. Se necesitan más investigaciones para superar desafíos técnicos, éticos y de privacidad y validar la aplicabilidad clínica de estas tecnologías.
Palabras clave
Referencias
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