Use of a Novel Artificial Intelligence Approach for a Faster and More Precise Computerized Facial Evaluation in Aesthetic Dentistry.
Introduction: AI is based on automated learning algorithms that use large bodies of information (big data). In the field of dentistry, AI allows the analysis of radiographs, intraoral images and other clinical recordings with unprecedented precision and speed. Facial analysis is known for helping de...
| Publicado en: | Journal of Esthetic & Restorative Dentistry Vol. 37; no. 2; pp. 346 - 352 |
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| Autores principales: | , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183951158&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183951158 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14964155 9OJ jtl: Journal of Esthetic & Restorative Dentistry issn: 14964155 maglogo: Y pubinfo: dt: Feb2025 vid: 37 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 183951158 180196855 183951158 183951158 10.1111/jerd.13320 183951158 ppf: 346 ppct: 6 formats: tig: atl: Use of a Novel Artificial Intelligence Approach for a Faster and More Precise Computerized Facial Evaluation in Aesthetic Dentistry. aug: au: Maniega‐Mañes, Irene Monterde‐Hernández, Manuel Mora‐Barrios, Karla Boquete‐Castro, Ana affil: Universidad Católica de Valencia, Valencia, Spain sug: subj: Aesthetics, Dental Facial Profile Artificial Intelligence Utilization Human Neural Networks (Computer) Descriptive Research Descriptive Statistics Analysis of Variance Algorithms T-Tests ab: Introduction: AI is based on automated learning algorithms that use large bodies of information (big data). In the field of dentistry, AI allows the analysis of radiographs, intraoral images and other clinical recordings with unprecedented precision and speed. Facial analysis is known for helping dentists and patients achieve a satisfactory result when a restorative treatment must be realized. The objective of this study is to conduct a neural network‐based computerized facial analysis using Python programming language in order to valuate its efficacy in facial point detection. Methods: The neural network was trained to identify the main facial and dental points: smile line, lips, size and for of the teeth, etc. A facial analysis was carried out using AI. A descriptive analysis was made with calculation of the mean and standard deviation (SD) of the precision and accuracy in each group. Analysis of variance (ANOVA) was used for the comparison of means between groups. Results: At the intersecting point between dentistry and technology, advances in artificial intelligence (AI) are producing a change in the way modern dentistry is performed. The present study evidenced lesser variability in the execution times of the neural network compared with the DSD system. This indicates that the neural network affords more consistent and predictable results, representing a significant advantage in terms of time and efficacy. Conclusion: The neural network is significantly more efficient and consistent in performing facial analyses than the conventional DSD system. The neural network reduces the time needed to complete the analysis and shows lesser variability in its execution times. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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