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

dc.contributor.authorBornes, Rita Silva
dc.contributor.authorMontero, Javier
dc.contributor.authorCorreia, André Ricardo Maia
dc.contributor.authorRosa, Nuno Ricardo das Neves
dc.date.accessioned2023-02-01T09:02:59Z
dc.date.available2023-02-01T09:02:59Z
dc.date.issued2023-02-01
dc.description.abstractStatement 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.prosdent.2022.12.011pt_PT
dc.identifier.eid85147223022
dc.identifier.issn0022-3913
dc.identifier.pmid36710172
dc.identifier.urihttp://hdl.handle.net/10400.14/40060
dc.identifier.wos000945881800001
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.titleUse of bioinformatic strategies as a predictive tool in implant-supported oral rehabilitation: a scoping reviewpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage322.e8
oaire.citation.issue2
oaire.citation.startPage322.e1
oaire.citation.titleJournal of Prosthetic Dentistrypt_PT
oaire.citation.volume129
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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