Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress

Detalhes bibliográficos
Autor(a) principal: Martins, Mónica Vieira
Data de Publicação: 2023
Outros Autores: Baptista, Luís, Henrique, Luís, Assunção, Victor, Araújo, Mário-Rui, Realinho, Valentim
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.8/9196
Resumo: first_pagesettingsOrder Article Reprints Open AccessReview Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress by Mónica Vieira Martins 1,*ORCID,Luís Baptista 1ORCID,Henrique Luís 1,2,3,4,Victor Assunção 1,2,3,4,Mário-Rui Araújo 1ORCID andValentim Realinho 1,5ORCID 1 Polytechnic Institute of Portalegre, 7300-110 Portalegre, Portugal 2 Faculdade de Medicina Dentária, Universidade de Lisboa, Unidade de Investigação em Ciências Orais e Biomédicas (UICOB), Rua Professora Teresa Ambrósio, 1600-277 Lisboa, Portugal 3 Faculdade de Medicina Dentária, Universidade de Lisboa, Rede de Higienistas Orais para o Desenvolvimento da Ciência (RHODes), Rua Professora Teresa Ambrósio, 1600-277 Lisboa, Portugal 4 Center for Innovative Care and Health Technology (ciTechcare), Polytechnic of Leiria, 2410-541 Leiria, Portugal 5 VALORIZA—Research Center for Endogenous Resource Valorization, 7300-555 Portalegre, Portugal * Author to whom correspondence should be addressed. Computation 2023, 11(6), 115; https://doi.org/10.3390/computation11060115 Submission received: 8 May 2023 / Revised: 5 June 2023 / Accepted: 8 June 2023 / Published: 10 June 2023 (This article belongs to the Special Issue Computational Medical Image Analysis) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract The past few decades have witnessed remarkable progress in the application of artificial intelligence (AI) and machine learning (ML) in medicine, notably in medical imaging. The application of ML to dental and oral imaging has also been developed, powered by the availability of clinical dental images. The present work aims to investigate recent progress concerning the application of ML in the diagnosis of oral diseases using oral X-ray imaging, namely the quality and outcome of such methods. The specific research question was developed using the PICOT methodology. The review was conducted in the Web of Science, Science Direct, and IEEE Xplore databases, for articles reporting the use of ML and AI for diagnostic purposes in X-ray-based oral imaging. Imaging types included panoramic, periapical, bitewing X-ray images, and oral cone beam computed tomography (CBCT). The search was limited to papers published in the English language from 2018 to 2022. The initial search included 104 papers that were assessed for eligibility. Of these, 22 were included for a final appraisal. The full text of the articles was carefully analyzed and the relevant data such as the clinical application, the ML models, the metrics used to assess their performance, and the characteristics of the datasets, were registered for further analysis. The paper discusses the opportunities, challenges, and limitations found.
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spelling Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent ProgressMónica Vieira Martins 1,* , Luís Baptista 1 , Henrique Luís 1,2,3,4, Victor Assunção 1,2,3,4, Mário-Rui Araújo 1 and Valentim Realinho 1,5 1Machine learningArtificial intelligenceOral healthX-ray imagingDiagnosisConvolutional neural networksDeep learningfirst_pagesettingsOrder Article Reprints Open AccessReview Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress by Mónica Vieira Martins 1,*ORCID,Luís Baptista 1ORCID,Henrique Luís 1,2,3,4,Victor Assunção 1,2,3,4,Mário-Rui Araújo 1ORCID andValentim Realinho 1,5ORCID 1 Polytechnic Institute of Portalegre, 7300-110 Portalegre, Portugal 2 Faculdade de Medicina Dentária, Universidade de Lisboa, Unidade de Investigação em Ciências Orais e Biomédicas (UICOB), Rua Professora Teresa Ambrósio, 1600-277 Lisboa, Portugal 3 Faculdade de Medicina Dentária, Universidade de Lisboa, Rede de Higienistas Orais para o Desenvolvimento da Ciência (RHODes), Rua Professora Teresa Ambrósio, 1600-277 Lisboa, Portugal 4 Center for Innovative Care and Health Technology (ciTechcare), Polytechnic of Leiria, 2410-541 Leiria, Portugal 5 VALORIZA—Research Center for Endogenous Resource Valorization, 7300-555 Portalegre, Portugal * Author to whom correspondence should be addressed. Computation 2023, 11(6), 115; https://doi.org/10.3390/computation11060115 Submission received: 8 May 2023 / Revised: 5 June 2023 / Accepted: 8 June 2023 / Published: 10 June 2023 (This article belongs to the Special Issue Computational Medical Image Analysis) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract The past few decades have witnessed remarkable progress in the application of artificial intelligence (AI) and machine learning (ML) in medicine, notably in medical imaging. The application of ML to dental and oral imaging has also been developed, powered by the availability of clinical dental images. The present work aims to investigate recent progress concerning the application of ML in the diagnosis of oral diseases using oral X-ray imaging, namely the quality and outcome of such methods. The specific research question was developed using the PICOT methodology. The review was conducted in the Web of Science, Science Direct, and IEEE Xplore databases, for articles reporting the use of ML and AI for diagnostic purposes in X-ray-based oral imaging. Imaging types included panoramic, periapical, bitewing X-ray images, and oral cone beam computed tomography (CBCT). The search was limited to papers published in the English language from 2018 to 2022. The initial search included 104 papers that were assessed for eligibility. Of these, 22 were included for a final appraisal. The full text of the articles was carefully analyzed and the relevant data such as the clinical application, the ML models, the metrics used to assess their performance, and the characteristics of the datasets, were registered for further analysis. The paper discusses the opportunities, challenges, and limitations found.MDPIIC-OnlineMartins, Mónica VieiraBaptista, LuísHenrique, LuísAssunção, VictorAraújo, Mário-RuiRealinho, Valentim2024-01-08T11:15:28Z2023-06-102023-06-10T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.8/9196engMartins, M.V.; Baptista, L.; Luís, H.; Assunção, V.; Araújo, M.-R.; Realinho, V. Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress. Computation 2023, 11, 115. https://doi.org/10.3390/computation110601152079-3197https://doi.org/10.3390/computation11060115info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-17T15:59:02Zoai:iconline.ipleiria.pt:10400.8/9196Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:51:45.713275Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress
Mónica Vieira Martins 1,* , Luís Baptista 1 , Henrique Luís 1,2,3,4, Victor Assunção 1,2,3,4, Mário-Rui Araújo 1 and Valentim Realinho 1,5 1
title Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress
spellingShingle Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress
Martins, Mónica Vieira
Machine learning
Artificial intelligence
Oral health
X-ray imaging
Diagnosis
Convolutional neural networks
Deep learning
title_short Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress
title_full Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress
title_fullStr Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress
title_full_unstemmed Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress
title_sort Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress
author Martins, Mónica Vieira
author_facet Martins, Mónica Vieira
Baptista, Luís
Henrique, Luís
Assunção, Victor
Araújo, Mário-Rui
Realinho, Valentim
author_role author
author2 Baptista, Luís
Henrique, Luís
Assunção, Victor
Araújo, Mário-Rui
Realinho, Valentim
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv IC-Online
dc.contributor.author.fl_str_mv Martins, Mónica Vieira
Baptista, Luís
Henrique, Luís
Assunção, Victor
Araújo, Mário-Rui
Realinho, Valentim
dc.subject.por.fl_str_mv Machine learning
Artificial intelligence
Oral health
X-ray imaging
Diagnosis
Convolutional neural networks
Deep learning
topic Machine learning
Artificial intelligence
Oral health
X-ray imaging
Diagnosis
Convolutional neural networks
Deep learning
description first_pagesettingsOrder Article Reprints Open AccessReview Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress by Mónica Vieira Martins 1,*ORCID,Luís Baptista 1ORCID,Henrique Luís 1,2,3,4,Victor Assunção 1,2,3,4,Mário-Rui Araújo 1ORCID andValentim Realinho 1,5ORCID 1 Polytechnic Institute of Portalegre, 7300-110 Portalegre, Portugal 2 Faculdade de Medicina Dentária, Universidade de Lisboa, Unidade de Investigação em Ciências Orais e Biomédicas (UICOB), Rua Professora Teresa Ambrósio, 1600-277 Lisboa, Portugal 3 Faculdade de Medicina Dentária, Universidade de Lisboa, Rede de Higienistas Orais para o Desenvolvimento da Ciência (RHODes), Rua Professora Teresa Ambrósio, 1600-277 Lisboa, Portugal 4 Center for Innovative Care and Health Technology (ciTechcare), Polytechnic of Leiria, 2410-541 Leiria, Portugal 5 VALORIZA—Research Center for Endogenous Resource Valorization, 7300-555 Portalegre, Portugal * Author to whom correspondence should be addressed. Computation 2023, 11(6), 115; https://doi.org/10.3390/computation11060115 Submission received: 8 May 2023 / Revised: 5 June 2023 / Accepted: 8 June 2023 / Published: 10 June 2023 (This article belongs to the Special Issue Computational Medical Image Analysis) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract The past few decades have witnessed remarkable progress in the application of artificial intelligence (AI) and machine learning (ML) in medicine, notably in medical imaging. The application of ML to dental and oral imaging has also been developed, powered by the availability of clinical dental images. The present work aims to investigate recent progress concerning the application of ML in the diagnosis of oral diseases using oral X-ray imaging, namely the quality and outcome of such methods. The specific research question was developed using the PICOT methodology. The review was conducted in the Web of Science, Science Direct, and IEEE Xplore databases, for articles reporting the use of ML and AI for diagnostic purposes in X-ray-based oral imaging. Imaging types included panoramic, periapical, bitewing X-ray images, and oral cone beam computed tomography (CBCT). The search was limited to papers published in the English language from 2018 to 2022. The initial search included 104 papers that were assessed for eligibility. Of these, 22 were included for a final appraisal. The full text of the articles was carefully analyzed and the relevant data such as the clinical application, the ML models, the metrics used to assess their performance, and the characteristics of the datasets, were registered for further analysis. The paper discusses the opportunities, challenges, and limitations found.
publishDate 2023
dc.date.none.fl_str_mv 2023-06-10
2023-06-10T00:00:00Z
2024-01-08T11:15:28Z
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language eng
dc.relation.none.fl_str_mv Martins, M.V.; Baptista, L.; Luís, H.; Assunção, V.; Araújo, M.-R.; Realinho, V. Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress. Computation 2023, 11, 115. https://doi.org/10.3390/computation11060115
2079-3197
https://doi.org/10.3390/computation11060115
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