Evaluation metric of smile classification by peri-oral tissue segmentation for the automation of digital smile design

Lee, Seulgi; Jin, Gan; Park, Ji-Hyun; Jung, Hoi-In; Kim, Jong-Eun

doi:10.1016/j.jdent.2024.104871

Journal of Dentistry

2024

DOI: 10.1016/j.jdent.2024.104871

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Evaluation metric of smile classification by peri-oral tissue segmentation for the automation of digital smile design

Seulgi Lee,

Gan Jin,

Ji-Hyun Park

et al.

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2024

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Use of a Novel Artificial Intelligence Approach for a Faster and More Precise Computerized Facial Evaluation in Aesthetic Dentistry

Maniega‐Mañes,

Monterde‐Hernández,

Mora‐Barrios

et al. 2024

J Esthet Restor Dent

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IntroductionAI 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.MethodsThe 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.ResultsAt 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.ConclusionThe 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.

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Use of a Novel Artificial Intelligence Approach for a Faster and More Precise Computerized Facial Evaluation in Aesthetic Dentistry

Maniega‐Mañes,

Monterde‐Hernández,

Mora‐Barrios

et al. 2024

J Esthet Restor Dent

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Evaluation metric of smile classification by peri-oral tissue segmentation for the automation of digital smile design

Cited by 1 publication

References 40 publications

Use of a Novel Artificial Intelligence Approach for a Faster and More Precise Computerized Facial Evaluation in Aesthetic Dentistry

Use of a Novel Artificial Intelligence Approach for a Faster and More Precise Computerized Facial Evaluation in Aesthetic Dentistry

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