Demystifying artificial intelligence and deep learning in dentistry

Detalhes bibliográficos
Autor(a) principal: RODRIGUES,Jonas Almeida
Data de Publicação: 2021
Outros Autores: KROIS,Joachim, SCHWENDICKE,Falk
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Brazilian Oral Research
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-83242021000100604
Resumo: Abstract Artificial intelligence (AI) is a general term used to describe the development of computer systems which can perform tasks that normally require human cognition. Machine learning (ML) is one subfield of AI, where computers learn rules from data, capturing its intrinsic statistical patterns and structures. Neural networks (NNs) have been increasingly employed for ML complex data. The application of multilayered NN is referred to as “deep learning”, which has been recently investigated in dentistry. Convolutional neural networks (CNNs) are mainly used for processing large and complex imagery data, as they are able to extract image features like edges, corners, shapes, and macroscopic patterns using layers of filters. CNN algorithms allow to perform tasks like image classification, object detection and segmentation. The literature involving AI in dentistry has increased rapidly, so a methodological guidance for designing, conducting and reporting studies must be rigorously followed, including the improvement of datasets. The limited interaction between the dental field and the technical disciplines, however, remains a hurdle for applicable dental AI. Similarly, dental users must understand why and how AI applications work and decide to appraise their decisions critically. Generalizable and robust AI applications will eventually prove helpful for clinicians and patients alike.
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spelling Demystifying artificial intelligence and deep learning in dentistryArtificial IntelligenceDeep LearningNeural Networks, ComputerDiagnostic ImagingDentistryAbstract Artificial intelligence (AI) is a general term used to describe the development of computer systems which can perform tasks that normally require human cognition. Machine learning (ML) is one subfield of AI, where computers learn rules from data, capturing its intrinsic statistical patterns and structures. Neural networks (NNs) have been increasingly employed for ML complex data. The application of multilayered NN is referred to as “deep learning”, which has been recently investigated in dentistry. Convolutional neural networks (CNNs) are mainly used for processing large and complex imagery data, as they are able to extract image features like edges, corners, shapes, and macroscopic patterns using layers of filters. CNN algorithms allow to perform tasks like image classification, object detection and segmentation. The literature involving AI in dentistry has increased rapidly, so a methodological guidance for designing, conducting and reporting studies must be rigorously followed, including the improvement of datasets. The limited interaction between the dental field and the technical disciplines, however, remains a hurdle for applicable dental AI. Similarly, dental users must understand why and how AI applications work and decide to appraise their decisions critically. Generalizable and robust AI applications will eventually prove helpful for clinicians and patients alike.Sociedade Brasileira de Pesquisa Odontológica - SBPqO2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-83242021000100604Brazilian Oral Research v.35 2021reponame:Brazilian Oral Researchinstname:Sociedade Brasileira de Pesquisa Odontológica (SBPqO)instacron:SBPQO10.1590/1807-3107bor-2021.vol35.0094info:eu-repo/semantics/openAccessRODRIGUES,Jonas AlmeidaKROIS,JoachimSCHWENDICKE,Falkeng2021-08-11T00:00:00Zoai:scielo:S1806-83242021000100604Revistahttps://www.scielo.br/j/bor/https://old.scielo.br/oai/scielo-oai.phppob@edu.usp.br||bor@sbpqo.org.br1807-31071806-8324opendoar:2021-08-11T00:00Brazilian Oral Research - Sociedade Brasileira de Pesquisa Odontológica (SBPqO)false
dc.title.none.fl_str_mv Demystifying artificial intelligence and deep learning in dentistry
title Demystifying artificial intelligence and deep learning in dentistry
spellingShingle Demystifying artificial intelligence and deep learning in dentistry
RODRIGUES,Jonas Almeida
Artificial Intelligence
Deep Learning
Neural Networks, Computer
Diagnostic Imaging
Dentistry
title_short Demystifying artificial intelligence and deep learning in dentistry
title_full Demystifying artificial intelligence and deep learning in dentistry
title_fullStr Demystifying artificial intelligence and deep learning in dentistry
title_full_unstemmed Demystifying artificial intelligence and deep learning in dentistry
title_sort Demystifying artificial intelligence and deep learning in dentistry
author RODRIGUES,Jonas Almeida
author_facet RODRIGUES,Jonas Almeida
KROIS,Joachim
SCHWENDICKE,Falk
author_role author
author2 KROIS,Joachim
SCHWENDICKE,Falk
author2_role author
author
dc.contributor.author.fl_str_mv RODRIGUES,Jonas Almeida
KROIS,Joachim
SCHWENDICKE,Falk
dc.subject.por.fl_str_mv Artificial Intelligence
Deep Learning
Neural Networks, Computer
Diagnostic Imaging
Dentistry
topic Artificial Intelligence
Deep Learning
Neural Networks, Computer
Diagnostic Imaging
Dentistry
description Abstract Artificial intelligence (AI) is a general term used to describe the development of computer systems which can perform tasks that normally require human cognition. Machine learning (ML) is one subfield of AI, where computers learn rules from data, capturing its intrinsic statistical patterns and structures. Neural networks (NNs) have been increasingly employed for ML complex data. The application of multilayered NN is referred to as “deep learning”, which has been recently investigated in dentistry. Convolutional neural networks (CNNs) are mainly used for processing large and complex imagery data, as they are able to extract image features like edges, corners, shapes, and macroscopic patterns using layers of filters. CNN algorithms allow to perform tasks like image classification, object detection and segmentation. The literature involving AI in dentistry has increased rapidly, so a methodological guidance for designing, conducting and reporting studies must be rigorously followed, including the improvement of datasets. The limited interaction between the dental field and the technical disciplines, however, remains a hurdle for applicable dental AI. Similarly, dental users must understand why and how AI applications work and decide to appraise their decisions critically. Generalizable and robust AI applications will eventually prove helpful for clinicians and patients alike.
publishDate 2021
dc.date.none.fl_str_mv 2021-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1807-3107bor-2021.vol35.0094
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dc.publisher.none.fl_str_mv Sociedade Brasileira de Pesquisa Odontológica - SBPqO
publisher.none.fl_str_mv Sociedade Brasileira de Pesquisa Odontológica - SBPqO
dc.source.none.fl_str_mv Brazilian Oral Research v.35 2021
reponame:Brazilian Oral Research
instname:Sociedade Brasileira de Pesquisa Odontológica (SBPqO)
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reponame_str Brazilian Oral Research
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