2023
DOI: 10.3389/fdmed.2023.1123976
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Materials informatics for developing new restorative dental materials: a narrative review

Abstract: Materials informatics involves the application of computational methodologies to process and interpret scientific and engineering data concerning materials. Although this concept has been well established in the fields of biology, drug discovery, and classic materials research, its application in the field of dental materials is still in its infancy. This narrative review comprehensively summarizes the advantages, limitations, and future perspectives of materials informatics from 2003 to 2022 for exploring the… Show more

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Cited by 6 publications
(3 citation statements)
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“…Mechanical tests were carried out for tension, compression, and three-point bending. In this paper, only the three-point bending data are reported, since they are more informative for the dental materials [ 23 ]. The key parameters to be evaluated are flexural strength, flexural modulus, and flexural strain.…”
Section: Methodsmentioning
confidence: 99%
“…Mechanical tests were carried out for tension, compression, and three-point bending. In this paper, only the three-point bending data are reported, since they are more informative for the dental materials [ 23 ]. The key parameters to be evaluated are flexural strength, flexural modulus, and flexural strain.…”
Section: Methodsmentioning
confidence: 99%
“…The primary objective is to conserve the maximum amount of the patient’s natural tooth structure. Atraumatic restorative therapy (ART) is a notable example of how minimal intervention dentistry puts this concept into practice [ 2 ]. Using manual instruments, the process involves the removal of soft, fully demineralized carious tooth tissue as part of an alternative restorative treatment [ 3 ].…”
Section: Introductionmentioning
confidence: 99%
“…These applications typically range from deep learning [4,5], for image-based tasks [6,7,8], to more recent natural language processing adaptations to aid in design of new materials [9,10,11]. Although machine learning approaches to materials discovery have found applications across multiple materials domains, it has yet to make an impact in the field of dental materials [12,13]. Instead, machine learning in dentistry has had modest impact in the areas of detection and diagnostics, with a few examples being the identification of dental caries, vertical root fractures, apical lesions, and diseases of the salivary glands [14].…”
Section: Introductionmentioning
confidence: 99%