学術雑誌論文

学術雑誌論文

text

Journal of Dental Research. 2022, 101(11), p. 1363-1371

Interpretable AI Explores Effective Components of CAD/CAM Resin Composites

SAGE Publications

英語

text

2022-04-15

https://doi.org/10.1177/00220345221089251

Li, H., T. Sakai, A. Tanaka, M. Ogura, C. Lee, S. Yamaguchi, and S. Imazato. “Interpretable AI Explores Effective Components of CAD/CAM Resin Composites.” Journal of Dental Research, (April 2022). https://doi.org/10.1177/00220345221089251.

High flexural strength of computer-aided manufacturing resin composite blocks (CAD/CAM RCBs) are required in clinical scenarios. However, the conventional in vitro approach of modifying materials’ composition by trial and error was not efficient to explore the effective components that contribute to the flexural strength. Machine learning (ML) is a powerful tool to achieve the above goals. Therefore, the aim of this study was to develop ML models to predict the flexural strength of CAD/CAM RCBs and explore the components that affect flexural strength as the first step. The composition of 12 commercially available products and flexural strength were collected from the manufacturers and literature. The initial data consisted of 16 attributes and 12 samples. Considering that the input data for each sample were recognized as a multidimensional vector, a fluctuation range of 0.1 was proposed for each vector and the number of samples was augmented to 120. Regression algorithms—that is, random forest (RF), extra trees, gradient boosting decision tree, light gradient boosting machine, and extreme gradient boosting—were used to develop 5 ML models to predict flexural strength. An exhaustive search and feature importance analysis were conducted to analyze the effective components that affected flexural strength. The R2 values for each model were 0.947, 0.997, 0.998, 0.983, and 0.927, respectively. The relative errors of all the algorithms were within 15%. Among the high predicted flexural strength group in the exhaustive search, urethane dimethacrylate was contained in all compositions. Filler content and triethylene glycol dimethacrylate were the top 2 features predicted by all models in the feature importance analysis. ZrSiO4 was the third important feature for all models, except the RF model. The ML models established in this study successfully predicted the flexural strength of CAD/CAM RCBs and identified the effective components that affected flexural strength based on the available data set.

The final version of this paper has been published in Journal of Dental Research, April 15, 2022 by SAGE Publications Ltd, All rights reserved. © Authors, 2022. It is available at: http://journals.sagepub.com/home/JDR.

88501

Interpretable AI Explores Effective Components of CAD/CAM Resin Composites

SAGE Publications

英語

2022-04-15

Journal of Dental Research

101

11

1363

1371

https://doi.org/10.1177/00220345221089251

https://hdl.handle.net/11094/88501