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dc.contributor.authorShafi, Imran
dc.contributor.authorFatima, Anum
dc.contributor.authorAfzal, Hammad
dc.contributor.authorTorre Díez, Isabel de la 
dc.contributor.authorLipari, Vivian
dc.contributor.authorBreñosa, Jose
dc.contributor.authorAshraf, Imran
dc.date.accessioned2024-03-01T12:27:59Z
dc.date.available2024-03-01T12:27:59Z
dc.date.issued2023
dc.identifier.citationDiagnostics, 2023, Vol. 13, Nº. 13, 2196es
dc.identifier.issn2075-4418es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/66484
dc.descriptionProducción Científicaes
dc.description.abstractArtificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherMDPIes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMedical informaticses
dc.subjectMedicina - Informáticaes
dc.subjectDentistryes
dc.subjectOdontologíaes
dc.subjectMedical carees
dc.subjectMachine learninges
dc.subjectArtificial intelligencees
dc.subjectImage processinges
dc.subjectImágenes, Tratamiento de lases
dc.subjectDiagnostic imaginges
dc.subjectImágenes médicases
dc.titleA comprehensive review of recent advances in artificial intelligence for dentistry E-healthes
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2023 The authorses
dc.identifier.doi10.3390/diagnostics13132196es
dc.relation.publisherversionhttps://www.mdpi.com/2075-4418/13/13/2196es
dc.identifier.publicationfirstpage2196es
dc.identifier.publicationissue13es
dc.identifier.publicationtitleDiagnosticses
dc.identifier.publicationvolume13es
dc.peerreviewedSIes
dc.identifier.essn2075-4418es
dc.rightsAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones
dc.subject.unesco1203.17 Informáticaes
dc.subject.unesco1203.04 Inteligencia Artificiales
dc.subject.unesco32 Ciencias Médicases
dc.subject.unesco3212 Salud Publicaes


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