Department of Prosthodontics, College of Dentistry, Yonsei University, Seoul 03722, Republic of Korea; Department of Mechanical Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
Department of Prosthodontics, College of Dentistry, Yonsei University, Seoul 03722, Republic of Korea.
J Dent. 2024 Jun;145:104871. doi: 10.1016/j.jdent.2024.104871. Epub 2024 Feb 1.
This study aimed to develop and validate evaluation metric for an automated smile classification model termed the "smile index." This innovative model uses computational methods to numerically classify and analyze conventional smile types.
The datasets used in this study consisted of 300 images to verify, 150 images to validate, and 9 images to test the evaluation metric. Images were annotated using Labelme. Computational techniques were used to calculate smile index values for the study datasets, and the resulting values were evaluated in three stages.
The smile index successfully classified smile types using cutoff values of 0.0285 and 0.193. High accuracy (0.933) was achieved, along with an F1 score greater than 0.09. The smile index successfully reclassified smiles into six types (low, low-to-medium, medium, medium-to-high, high, and extremely high smiles), thereby providing a clear distinction among different smile characteristics.
The smile index is a novel dimensionless parameter for classifying smile types. The index acts as a robust evaluation tool for artificial intelligence models that automatically classify smile types, thereby providing a scientific basis for largely subjective aesthetic elements.
The computational approach employed by the smile index enables quantitative numerical classification of smile types. This fosters the application of computerized methods in quantifying and analyzing real smile characteristics observed in clinical practice, paving the way for a more objective evidence-based approach to aesthetic dentistry.
本研究旨在开发和验证一种名为“微笑指数”的自动微笑分类模型的评估指标。该创新模型使用计算方法对传统的微笑类型进行数值分类和分析。
本研究使用的数据集包括 300 张用于验证的图像、150 张用于验证的图像和 9 张用于测试评估指标的图像。图像使用 Labelme 进行标注。使用计算技术计算研究数据集的微笑指数值，并在三个阶段评估得到的数值。
微笑指数使用 0.0285 和 0.193 的截断值成功地对微笑类型进行了分类。实现了高准确度（0.933），F1 得分大于 0.09。微笑指数成功地将微笑重新分类为六种类型（低、低-中、中、中-高、高和极高），从而清晰地区分了不同的微笑特征。
微笑指数是一种用于分类微笑类型的新的无量纲参数。该指数是一种强大的评估工具，用于自动分类微笑类型的人工智能模型，为评估和分析美学元素提供了科学依据。
微笑指数采用的计算方法能够对微笑类型进行定量的数值分类。这促进了计算机方法在量化和分析临床实践中观察到的真实微笑特征方面的应用，为更客观的基于证据的美学牙科方法铺平了道路。
