Use of bioinformatic strategies as a predictive tool in implant-supported oral rehabilitation: a scoping review

Rita Silva Bornes; Javier Montero; André Ricardo Maia Correia; Nuno Ricardo das Neves Rosa

doi:10.1016/j.prosdent.2022.12.011

Use of bioinformatic strategies as a predictive tool in implant-supported oral rehabilitation: a scoping review

Rita Silva Bornes^*, Javier Montero, André Ricardo Maia Correia, Nuno Ricardo das Neves Rosa

^*Autor correspondente para este trabalho

Resultado de pesquisa › revisão de pares

5 Citações (Scopus)

23 Transferências (Pure)

Resumo

Statement of problem: The use of bioinformatic strategies is growing in dental implant protocols. The current expansion of Omics sciences and artificial intelligence (AI) algorithms in implant dentistry applications have not been documented and analyzed as a predictive tool for the success of dental implants. Purpose: The purpose of this scoping review was to analyze how artificial intelligence algorithms and Omics technologies are being applied in the field of oral implantology as a predictive tool for dental implant success. Material and methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist was followed. A search strategy was created at PubMed and Web of Science to answer the question “How is bioinformatics being applied in the area of oral implantology as a predictive tool for implant success?” Results: Thirteen articles were included in this review. Only 3 applied bioinformatic models combining AI algorithms and Omics technologies. These studies highlighted 2 key points for the creation of precision medicine: deep population phenotyping and the integration of Omics sciences in clinical protocols. Most of the studies identified applied AI only in the identification and classification of implant systems, quantification of peri-implant bone loss, and 3-dimensional bone analysis, planning implant placement. Conclusions: The conventional criteria currently used as a technique for the diagnosis and monitoring of dental implants are insufficient and have low accuracy. Models that apply AI algorithms combined with precision methodologies—biomarkers—are extremely useful in the creation of precision medicine, allowing medical dentists to forecast the success of the implant. Tools that integrate the different types of data, including imaging, molecular, risk factor, and implant characteristics, are needed to make a more accurate and personalized prediction of implant success.

Idioma original	English
Páginas (de-até)	322.e1-322.e8
Número de páginas	8
Revista	Journal of Prosthetic Dentistry
Volume	129
Número de emissão	2
DOIs	https://doi.org/10.1016/j.prosdent.2022.12.011
Estado da publicação	Publicado - 1 fev. 2023

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Acesso ao documento

10.1016/j.prosdent.2022.12.011Licença:

61914513Final published version, 702 KBLicença:

http://hdl.handle.net/10400.14/40060Licença:

Outros arquivos e links

Link to publication in Scopus

CIIS: Centro de Investigação Interdisciplinar em Saúde
Barros, M. (PI), Rosa, N. (Investigador), Correia, M. J. (Investigador), Caldas, A. C. (Investigador), Amado, J. C. (Investigador), Figueiredo, A. S. (Investigador), Esteves, A. C. (Investigador), Mineiro, A. (Investigador), Abreu, A. M. (Investigador), Duarte, A. S. (Investigador), Almeida, S. F. (Investigador), Correia, A. (Investigador), Moura, A. (Investigador), Almeida, A. (Investigador), Araújo, B. (Investigador), Moura-Netto, C. (Investigador), Ferrito, C. (Investigador), Pais-Vieira, C. (Investigador), Festas, C. (Investigador), Marques-Vieira, C. (Investigador), Catré, D. (Investigador), Nunes, E. (Investigador), Jesus, É. (Investigador), Ribeiro, F. (Investigador), Rosário, F. (Investigador), Fernandes, G. (Investigador), Rato, J. R. (Bolseiro), Salgado, J. R. (Investigador), Neves-Amado, J. (Investigador), Amendoeira, J. (Investigador), Sá, L. (Investigador), Capelas, M. L. (Investigador), Vieira, M. M. (Investigador), Nunes, M. V. S. (Investigador), Cardoso, M. (Investigador), Veiga, N. J. (Investigador), Fonseca, P. (Investigador), Correia, P. N. (Investigador), Couto, P. (Investigador), Pontífice-Sousa, P. (Investigador), Ravasco, P. (Investigador), Carvalho, P. V. D. (Investigador), Alves, P. (Investigador), Melo, P. (Investigador), Silva, R. (Investigador), Canaipa, R. (Investigador), Noites, R. (Investigador), Rio, R. (Investigador), Almeida, S. (Investigador), Deodato, S. (Investigador), Caldeira, S. (Investigador), Silva, S. (Investigador), Borges, T. (Investigador), Silva, V. (Investigador), Charepe, Z. (Investigador), Rodrigues-Pires, F. (Voluntário), Veludo, F. (Investigador), Carmo, H. (Bolseiro), Romeiro, J. (Estudante), Melo, M. (Investigador), Braga, M. (Investigador), Amaral, T. (Investigador), Moreira, M. A. (Investigador), Guerra, N. (Estudante), Santos, P. (Investigador), Paço, S. (Estudante), Lynce, S. (Bolseiro), Miguel, S. (Estudante), Costa, T. (Investigador), Silva-Neves, V. (Investigador), Silva, A. (Investigador), Carvalho, A. R. (Investigador), Almeida, B. (Investigador), Figueiredo, C. (Investigador), Esteves, E. (Bolseiro), Araújo, F. M. (Investigador), Garcia, J. G. (Investigador), Santos, L. (Investigador), Santos, N. M. D. (Investigador), Lopes, P. (Investigador), Bornes, R. (Investigador), Silva, R. (Investigador), Costa, S. (Investigador), Silva, S. M. (Investigador), Marques, T. (Investigador), Almeida, A. (Investigador), Santos, M. (Bolseiro), Santos, P. (Investigador), Miguel, S. (Investigador), Mendes, K. (Investigador), Gomes, A. P. (Investigador), Henriques, J. M. P. (Investigador), Costa, H. A. F. P. (Investigador) & Ribeiro, P. (Investigador)
Fundação para a Ciência e a Tecnologia
1/01/20 → 31/12/24
Projeto

Citação

@article{aaa7e2792492471daf0232a76e5c351c,

title = "Use of bioinformatic strategies as a predictive tool in implant-supported oral rehabilitation: a scoping review",

abstract = "Statement of problem: The use of bioinformatic strategies is growing in dental implant protocols. The current expansion of Omics sciences and artificial intelligence (AI) algorithms in implant dentistry applications have not been documented and analyzed as a predictive tool for the success of dental implants. Purpose: The purpose of this scoping review was to analyze how artificial intelligence algorithms and Omics technologies are being applied in the field of oral implantology as a predictive tool for dental implant success. Material and methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist was followed. A search strategy was created at PubMed and Web of Science to answer the question “How is bioinformatics being applied in the area of oral implantology as a predictive tool for implant success?” Results: Thirteen articles were included in this review. Only 3 applied bioinformatic models combining AI algorithms and Omics technologies. These studies highlighted 2 key points for the creation of precision medicine: deep population phenotyping and the integration of Omics sciences in clinical protocols. Most of the studies identified applied AI only in the identification and classification of implant systems, quantification of peri-implant bone loss, and 3-dimensional bone analysis, planning implant placement. Conclusions: The conventional criteria currently used as a technique for the diagnosis and monitoring of dental implants are insufficient and have low accuracy. Models that apply AI algorithms combined with precision methodologies—biomarkers—are extremely useful in the creation of precision medicine, allowing medical dentists to forecast the success of the implant. Tools that integrate the different types of data, including imaging, molecular, risk factor, and implant characteristics, are needed to make a more accurate and personalized prediction of implant success.",

author = "Bornes, {Rita Silva} and Javier Montero and Correia, {Andr{\'e} Ricardo Maia} and Rosa, {Nuno Ricardo das Neves}",

note = "Funding Information: Rita Silva Bornes: Conceptualization, Methodology, Validation, Visualization, Writing – original draft, preparation, Writing – review & editing, Project administration. Javier Montero: Conceptualization, Methodology, Validation, Visualization, Writing – original draft, preparation, Writing – review & editing, Project administration, Supervision. Andr{\'e} Ricardo Maia Correia: Conceptualization, Methodology, Validation, Visualization, Writing – original draft, preparation, Writing – review & editing, Project administration, Supervision, Funding acquisition. Nuno Ricardo das Neves Rosa: Conceptualization, Methodology, Validation, Visualization, Writing – original draft, preparation, Writing – review & editing, Project administration, Supervision, Funding acquisition. Publisher Copyright: {\textcopyright} 2023 The Authors",

year = "2023",

month = feb,

day = "1",

doi = "10.1016/j.prosdent.2022.12.011",

language = "English",

volume = "129",

pages = "322.e1--322.e8",

journal = "Journal of Prosthetic Dentistry",

issn = "0022-3913",

publisher = "Elsevier Inc.",

number = "2",

}

TY - JOUR

T1 - Use of bioinformatic strategies as a predictive tool in implant-supported oral rehabilitation

T2 - a scoping review

AU - Bornes, Rita Silva

AU - Montero, Javier

AU - Correia, André Ricardo Maia

AU - Rosa, Nuno Ricardo das Neves

N1 - Funding Information: Rita Silva Bornes: Conceptualization, Methodology, Validation, Visualization, Writing – original draft, preparation, Writing – review & editing, Project administration. Javier Montero: Conceptualization, Methodology, Validation, Visualization, Writing – original draft, preparation, Writing – review & editing, Project administration, Supervision. André Ricardo Maia Correia: Conceptualization, Methodology, Validation, Visualization, Writing – original draft, preparation, Writing – review & editing, Project administration, Supervision, Funding acquisition. Nuno Ricardo das Neves Rosa: Conceptualization, Methodology, Validation, Visualization, Writing – original draft, preparation, Writing – review & editing, Project administration, Supervision, Funding acquisition. Publisher Copyright: © 2023 The Authors

PY - 2023/2/1

Y1 - 2023/2/1

N2 - Statement of problem: The use of bioinformatic strategies is growing in dental implant protocols. The current expansion of Omics sciences and artificial intelligence (AI) algorithms in implant dentistry applications have not been documented and analyzed as a predictive tool for the success of dental implants. Purpose: The purpose of this scoping review was to analyze how artificial intelligence algorithms and Omics technologies are being applied in the field of oral implantology as a predictive tool for dental implant success. Material and methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist was followed. A search strategy was created at PubMed and Web of Science to answer the question “How is bioinformatics being applied in the area of oral implantology as a predictive tool for implant success?” Results: Thirteen articles were included in this review. Only 3 applied bioinformatic models combining AI algorithms and Omics technologies. These studies highlighted 2 key points for the creation of precision medicine: deep population phenotyping and the integration of Omics sciences in clinical protocols. Most of the studies identified applied AI only in the identification and classification of implant systems, quantification of peri-implant bone loss, and 3-dimensional bone analysis, planning implant placement. Conclusions: The conventional criteria currently used as a technique for the diagnosis and monitoring of dental implants are insufficient and have low accuracy. Models that apply AI algorithms combined with precision methodologies—biomarkers—are extremely useful in the creation of precision medicine, allowing medical dentists to forecast the success of the implant. Tools that integrate the different types of data, including imaging, molecular, risk factor, and implant characteristics, are needed to make a more accurate and personalized prediction of implant success.

AB - Statement of problem: The use of bioinformatic strategies is growing in dental implant protocols. The current expansion of Omics sciences and artificial intelligence (AI) algorithms in implant dentistry applications have not been documented and analyzed as a predictive tool for the success of dental implants. Purpose: The purpose of this scoping review was to analyze how artificial intelligence algorithms and Omics technologies are being applied in the field of oral implantology as a predictive tool for dental implant success. Material and methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist was followed. A search strategy was created at PubMed and Web of Science to answer the question “How is bioinformatics being applied in the area of oral implantology as a predictive tool for implant success?” Results: Thirteen articles were included in this review. Only 3 applied bioinformatic models combining AI algorithms and Omics technologies. These studies highlighted 2 key points for the creation of precision medicine: deep population phenotyping and the integration of Omics sciences in clinical protocols. Most of the studies identified applied AI only in the identification and classification of implant systems, quantification of peri-implant bone loss, and 3-dimensional bone analysis, planning implant placement. Conclusions: The conventional criteria currently used as a technique for the diagnosis and monitoring of dental implants are insufficient and have low accuracy. Models that apply AI algorithms combined with precision methodologies—biomarkers—are extremely useful in the creation of precision medicine, allowing medical dentists to forecast the success of the implant. Tools that integrate the different types of data, including imaging, molecular, risk factor, and implant characteristics, are needed to make a more accurate and personalized prediction of implant success.

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U2 - 10.1016/j.prosdent.2022.12.011

DO - 10.1016/j.prosdent.2022.12.011

M3 - Review article

C2 - 36710172

SN - 0022-3913

VL - 129

SP - 322.e1-322.e8

JO - Journal of Prosthetic Dentistry

JF - Journal of Prosthetic Dentistry

IS - 2

ER -

Use of bioinformatic strategies as a predictive tool in implant-supported oral rehabilitation: a scoping review

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CIIS: Centro de Investigação Interdisciplinar em Saúde

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