Inteligência artificial aplicada aos exames de imagem odontológicos: uma revisão da literatura
DOI:
https://doi.org/10.22456/2177-0018.128781Palavras-chave:
Aprendizado de máquina, Tomografia computadorizada de feixe cônico, Radiografia panorâmica, Radiografia dentária digital, Diagnóstico por imagemResumo
Objetivo: investigar a literatura relacionada à aplicação e desempenho da Inteligência Artificial (IA) em exames de imagem odontológicos. Revisão de literatura: foram incluídos 70 trabalhos experimentais e revisões sistemáticas da literatura, publicados em inglês, no período entre 2018 e 2021, que analisaram a aplicabilidade da IA na detecção automática de: pontos cefalométricos, lesões de cárie, lesões apicais, perda óssea periodontal, sistemas de implantes, cistos e tumores odontogênicos, osteoporose, sinusite maxilar, terceiros molares e canal mandibular, ateromas em carótida, fratura radicular vertical, osteoartrite em articulação temporomandibular, avaliação de morfologia radicular e numeração dentária. Resultados: 58,73% dos trabalhos analisados mostrou acurácia diagnóstica acima de 80% com a utilização de IA. Discussão: A maior limitação encontrada foi em relação à aquisição de amostras em quantidade suficiente para treinamento e teste dos modelos, já que imagens radiográficas têm sua disponibilidade limitada por questões éticas e legais relativas aos pacientes e Instituições. A falta de padronização na segmentação e processamento das imagens foi outro fator a influenciar os resultados obtidos, dificultando comparação e generalização. Apesar disso, diversos estudos apresentaram sugestões ou possíveis aperfeiçoamentos para pesquisas futuras, de forma a reduzir estas limitações. Conclusão: A aplicação da IA no diagnóstico por imagens mostrou-se promissora nas diversas áreas pesquisadas, com desempenhos muito semelhantes ou mesmo superiores, muitas vezes, ao desempenho dos profissionais humanos. Contudo, para a legitimação de sua utilização como parte do fluxo de trabalho na clínica, limitações ainda presentes devem ser superadas, especialmente no treinamento dos algoritmos para obtenção de melhores valores de acurácia.
Downloads
Referências
Tang A,Tam R, Cadrin-Chenevert A, Binge WG, Chong J, Barfett J, et al. Canadian association of radiologists white paper on artificial intelligence in radiology. Can Assoc Radiol J. 2018;69:120-35.
Park WJ, Park JB. History and application of artificial neural networks in dentistry. Eur J Dent. 2018 Oct-Dec;12(4):594-601.
Hwang JJ, Jung YH, Cho BH, Heo MS. An overview of deep learning in the field of dentistry. Imaging Sci Dent. 2019 Mar;49(1):1-7.
Chen S, Wang L, Li G, Wu TH, Diachina S, Tejera B, et al. Machine learning in orthodontics: introducing a 3D auto-segmentation and auto-landmark finder of CBCT images to assess maxillary constriction in unilateral impacted canine patients. Angle Orthod. 2020 Jan;90(1):77-84.
Nichols JA, Chan HWH, Baker MAB. Machine learning: applications of artificial intelligence to imaging and diagnosis. Biophys Rev. 2019 Feb;11(1):111-8.
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;77:106-11.
Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges. J Dent Res. 2020 Jul;99(7):769-74.
Hiraiwa T, Ariji Y, Fukuda M, Kise Y, Nakata K, Katsumata A, et al. A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofac Radiol. 2019 Mar;48(3):20180218.
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 Jan;49(1):20190107.
Leite AF, Vasconcelos KF, Willems H, Jacobs R. Radiomics and machine learning in oral healthcare. Proteomics Clin Appl. 2020 May;14(3):e1900040.
Poedjiastoeti W, Suebnukarn S. Application of convolutional neural network in the diagnosis of jaw tumors. Healthc Inform Res. 2018 Jul;24(3):236-41.
Kats L, Vered M, Zlotogorski-Hurvitz A, Harpaz I. Atherosclerotic carotid plaque on panoramic radiographs: neural network detection. Int J Comput Dent. 2019;22(2):163-9.
Tuzoff DV, Tuzova LN, Bornstein MM, Krasnov AS, Kharchenko MA, Nikolenko SI, et al. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofac Radiol. 2019 May;48(4):20180051.
Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, et al. Deep learning for the radiographic detection of apical lesions. J Endod. 2019 Jul;45(7):917-22.e5.
Kim J, Lee HS, Song IS, Jung KH. DeNTNet: deep neural transfer network for the detection of periodontal bone loss using panoramic dental radiographs. Sci Rep. 2019 Nov 26;9(1):17615-22.
Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, et al. Deep learning for the radiographic detection of periodontal bone loss. Sci Rep. 2019 Jun 11;9(1):8495.
Ariji Y, Yanashita Y, Kutsuna S, Muramatsu C, Fukuda M, Kise Y, et al. Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique. Oral Surg Oral Med Oral Pathol Oral Radiol. 2019 Oct;128(4):424-30.
Lee JS, Adhikari S, Liu L, Jeong HG, Kim H, Yoon SJ. Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminar study. Dentomaxillofac Radiol. 2019 Jan;48(1):20170344.
Murata M, Ariji Y, Ohashi Y, Kawai T, Fukuda M, Funakoshi T, et al. Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiol. 2019 Sep;35(3):301-7.
Vinayahalingam S, Xi T, Bergé S, Maal T, de Jong G. Automated detection of third molars and mandibular nerve by deep learning. Sci Rep. 2019 Jun 21;9(1):9007.
Kuwada C, Ariji Y, Fukuda M, Kise Y, Fujita H, Katsumata A, et al. Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020 Oct;130(4):464-9.
Fukuda M, Ariji Y, Kise Y, Nozawa M, Kuwada C, Funakoshi T, et al. Comparison of 3 deep learning neural networks for classifying the relationship between the mandibular third molar and the mandibular canal on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020 Sep;130(3):336-43.
Fukuda M, Inamoto K, Shibata N, Ariji Y, Yanashita Y, Kutsuna S, et al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiol. 2020 Oct;36(4):337-43.
Lee KS, Jung SK, Ryu JJ, Shin SW, Choi J. Evaluation of transfer learning with deep convolutional neural networks for screening osteoporosis in dental panoramic radiographs. J Clin Med. 2020 Feb 1;9(2):392.
Nakamoto T, Hatsuta S, Yagi S, Verdonschot RG, Taguchi A, Kakimoto N. Computer-aided diagnosis system for osteoporosis based on quantitative evaluation of mandibular lower border porosity using panoramic radiographs. Dentomaxillofac Radiol. 2020 May 1;49(4):20190481.
Endres MG, Hillen F, Salloumis M, Sedaghat AR, Niehues SM, Quatela O, et al. Development of a deep learning algorithm for periapical disease detection in dental radiographs. Diagnostics (Basel). 2020 Jun 24;10(6):430.
Chang HJ, Lee SJ, Yong TH, Shin NY, Jang BG, Kim JE, et al. Deep learning hybrid method to automatically diagnose periodontal bone loss and stage periodontitis. Sci Rep. 2020 May 5;10(1):7531.
Thanathornwong B, Suebnukarn S. Automatic detection of periodontal compromised teeth in digital panoramic radiographs using faster regional convolutional neural networks. Imaging Sci Dent. 2020 Jun;50(2):169-74.
Sukegawa S, Yoshii K, Hara T, Yamashita K, Nakano K, Yamamoto N, et al. Deep neural networks for dental implant system classification. Biomolecules. 2020 Jul 1;10(7):984.
Kwon O, Yong TH, Kang SR, Kim JE, Huh KH, Heo MS, et al. Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolutional neural network. Dentomaxillofac Radiol. 2020 Dec 1;49(8):20200185.
Yang H, Jo E, Kim HJ, Cha IH, Jung YS, Nam W, et al. Deep learning for automated detection of cyst and tumors of the jaw in panoramic radiographs. J Clin Med. 2020 Jun 12;9(6):1839.
Sukegawa S, Yoshii K, Hara T, Matsuyama T, Yamashita K, Nakano K, et al. Multi-task deep learning model for classification of dental implant brand and treatment stage using dental panoramic radiograph images. Biomolecules. 2021 May;11(6):815.
Lian L, Zhu T, Zhu F, Zhu H. Deep learning for caries detection and classification. Diagnostics (Basel). 2021 Sep;11(9):1672.
Yüksel AE, Gültekin S, Simsar E, Özdemir ŞD, Gündoğar M, Tokgöz SB, et al. Dental enumeration and multiple treatment detection on panoramic X-rays using deep learning. Sci Rep. 2021 Jun 11;11(1):12342.
Bilgir E, Bayrakdar İŞ, Çelik Ö, Orhan K, Akkoca F, Sağlam H, et al. An artificial intelligence approach to automatic tooth detection and numbering in panoramic radiographs. BMC Med Imaging. 2021 Aug;21(1):124.
Liu Z, Liu J, Zhou Z, Zhang Q, Wu H, Zhai G, et al. Differential diagnosis of ameloblastoma and odontogenic keratocyst by machine learning of panoramic radiographs. Int J Comput Assist Radiol Surg. 2021 Mar;16(3):415-22.
Ed-Dhahraouy M, Riri H, Ezzahmouly M, Bourzgui F, El Moutaoukkil A. A new methodology for automatic detection of reference points in 3D cephalometry: a pilot study. Int Orthod. 2018 Jun;16(2):328-37.
Montúfar J, Romero M, Scougall-Vilchis RJ. Automatic 3-dimensional cephalometric landmarking based on active shape models in related projections. Am J Orthod Dentofacial Orthop. 2018 Mar;153(3):449-58.
Montúfar J, Romero M, Scougall-Vilchis RJ. Hybrid approach for automatic cephalometric landmark annotation on cone-beam computed tomography volumes. Am J Orthod Dentofacial Orthop. 2018 Jul;154(1):140-50.
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 Feb;47(2):20170054.
Lee KS, Kwak HJ, Oh JM, Jha N, Kim YJ, Kim W, et al. Automated detection of TMJ osteoarthritis based on artificial intelligence. J Dent Res. 2020 Nov;99(12):1363-7.
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. Sci Rep. 2020 Apr;10(1):5842.
Kwak GH, Kwak EJ, Song JM, Park HR, Jung YH, Cho BH, et al. Automatic mandibular canal detection using a deep convolutional neural network. Sci Rep. 2020 Mar;10(1):5711.
Yun HS, Jang TJ, Lee SM, Lee SH, Seo JK. Learning-based local-to-global landmark annotation for automatic 3D cephalometry. Phys Med Biol. 2020 Apr;65(8):085018.
Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, Özyürek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int Endod J. 2020 May;53(5):680-9.
Setzer FC, Shi KJ, Zhang Z, Yan H, Yoon H, Mupparapu M, et al. Artificial intelligence for the computer-aided detection of periapical lesions in cone-beam computed tomographic images. J Endod. 2020 Jul;46(7):987-93.
Kim MJ, Liu Y, Oh SH, Ahn HW, Kim SH, Nelson G. Evaluation of a multi-stage convolutional neural network-based fully automated landmark identification system using cone-beam computed tomographysynthesized posteroanterior cephalometric images. Korean J Orthod. 2021 Mar;51(2):77-85.
Sherwood AA, Sherwood AI, Setzer FC, K SD, Shamili JV, John C, et al. A deep learning approach to segment and classify C-shaped canal morphologies in mandibular second molars using cone-beam computed tomography. J Endod. 2021 Dec;47(12):1907-16.
Lee JH, Kim DH, Jeong SN, Choi SH. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci. 2018 Apr;48(2):114-23.
Zhang K, Wu J, Chen H, Lyu P. An effective teeth recognition method using label tree with cascade network structure. Comput Med Imaging Graph. 2018 Sep;68:61-70.
Chen H, Zhang K, Lyu P, Li H, Zhang L, Wu J, et al. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Sci Rep. 2019 Mar;9(1):3840.
Kim JE, Nam NE, Shim JS, Jung YH, Cho BH, Hwang JJ. Transfer learning via deep neural networks for implant fixture system classification using periapical radiographs. J Clin Med. 2020 Apr;9(4):1117.
Geetha V, Aprameya KS, Hinduja DM. Dental caries diagnosis in digital radiographs using back-propagation neural network. Health Inf Sci Syst. 2020 Jan;8(1):8.
Danks RP, Bano S, Orishko A, Tan HJ, Moreno Sancho F, D'Aiuto F, et al. Automating periodontal bone loss measurement via dental landmark localisation. Int J Comput Assist Radiol Surg. 2021 Jul;16(7):1189-99.
Li CW, Lin SY, Chou HS, Chen TY, Chen YA, Liu SY, et al. Detection of dental apical lesions using CNNs on periapical radiograph. Sensors (Basel). 2021 Oct;21(21):7049.
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 Sep;100:103425.
Lee S, Oh SI, Jo J, Kang S, Shin Y, Park JW. Deep learning for early dental caries detection in bitewing radiographs. Sci Rep. 2021 Aug;11(1):16807.
Mao YC, Chen TY, Chou HS, Lin SY, Liu SY, Chen YA, et al. Caries and restoration detection using bitewing film based on transfer learning with CNNs. Sensors (Basel). 2021 Jul;21(13):4613.
Mertens S, Krois J, Cantu AG, Arsiwala LT, Schwendicke F. Artificial intelligence for caries detection: randomized trial. J Dent. 2021 Dec;115:103849.
Yasa Y, Çelik Ö, Bayrakdar IS, Pekince A, Orhan K, Akarsu S, et al. An artificial intelligence proposal to automatic teeth detection and numbering in dental bite-wing radiographs. Acta Odontol Scand. 2021 May;79(4):275-81.
Park JH, Hwang HW, Moon JH, Yu Y, Kim H, Her SB, et al. Automated identification of cephalometric landmarks: Part 1-Comparisons between the latest deep-learning methods YOLOV3 and SSD. Angle Orthod. 2019 Nov;89(6):903-9.
Kunz F, Stellzig-Eisenhauer A, Zeman F, Boldt J. Artificial intelligence in orthodontics: evaluation of a fully automated cephalometric analysis using a customized convolutional neural network. J Orofac Orthop. 2020 Jan;81(1):52-68.
Hwang HW, Park JH, Moon JH, Yu Y, Kim H, Her SB, et al. Automated identification of cephalometric landmarks: part 2-might it be better than human? Angle Orthod. 2020 Jan;90(1):69-76.
Kim J, Kim I, Kim YJ, Kim M, Cho JH, Hong M, et al. Accuracy of automated identification of lateral cephalometric landmarks using cascade convolutional neural networks on lateral cephalograms from nationwide multi-centres. Orthod Craniofac Res. 2021 Dec;24 Suppl 2:59-67.
Lee JH, Jeong SN. Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: a pilot study. Medicine (Baltimore). 2020 Jun;99(26):e20787.
Lee JH, Kim YT, Lee JB, Jeong SN. A performance comparison between automated deep learning and dental professionals in classification of dental implant systems from dental imaging: a multi-center study. Diagnostics (Basel). 2020 Nov;10(11):910.
Hadj Saïd M, Le Roux MK, Catherine JH, Lan R. Development of an artificial intelligence model to identify a dental implant from a radiograph. Int J Oral Maxillofac Implants. 2020 Nov/Dec;36(6):1077-82.
Patil S, Kulkarni V, Bhise A. Algorithmic analysis for dental caries detection using an adaptive neural network architecture. Heliyon. 2019 May;5(5):e01579.
Kim Y, Lee KJ, Sunwoo L, Choi D, Nam CM, Cho J, et al. Deep learning in diagnosis of maxillary sinusitis using conventional radiography. Invest Radiol. 2019 Jan;54(1):7-15.
Lee JH, Kim DH, Jeong SN. Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network. Oral Dis. 2020 Jan;26(1):152-8.
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Copyright (c) 2023 Jerusa Jobim Jardim, Heraldo Luís Dias da Silveira, Priscila Fernanda da Silveira Tiecher, Mariana Boessio Vizzotto:, Nádia Assein Arús
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
Todos os trabalhos publicados nesta revista adotam uma Licença Creative Commons Atribuição 4.0 (CC BY 4.0), e seu uso deve ser feito sob essas condições de licenciamento. Os autores concedem à revista o direito de primeira publicação e têm autorização para assumir contratos adicionais separadamente, para distribuição não exclusiva da versão do trabalho publicado nesta revista, como publicar em repositório institucional, com reconhecimento de autoria e publicação inicial.
