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.

在临床场景中需要计算机辅助制造树脂复合块 (CAD/CAM RCB) 的高抗弯强度。然而，通过反复试验来改变材料成分的常规体外方法对于探索有助于弯曲强度的有效成分并不有效。机器学习 (ML) 是实现上述目标的强大工具。因此，本研究的目的是开发 ML 模型来预测 CAD/CAM RCB 的抗弯强度，并作为第一步探索影响抗弯强度的组件。从制造商和文献中收集了 12 种市售产品的成分和抗弯强度。初始数据由 16 个属性和 12 个样本组成。考虑到每个样本的输入数据都被识别为一个多维向量，为每个向量提出了 0.1 的波动范围，并将样本数增加到 120 个。回归算法——即随机森林（RF）、额外树、梯度提升决策树、轻梯度提升机和极端梯度提升——被用来开发5个ML模型来预测弯曲强度。进行了详尽的搜索和特征重要性分析，以分析影响抗弯强度的有效成分。这 进行了详尽的搜索和特征重要性分析，以分析影响抗弯强度的有效成分。这 进行了详尽的搜索和特征重要性分析，以分析影响抗弯强度的有效成分。这每个模型的R ²值分别为 0.947、0.997、0.998、0.983 和 0.927。所有算法的相对误差在15%以内。在详尽搜索中的高预测弯曲强度组中，所有组合物中都含有氨基甲酸乙酯二甲基丙烯酸酯。填料含量和三甘醇二甲基丙烯酸酯是特征重要性分析中所有模型预测的前 2 位特征。ZrSiO ₄是除 RF 模型之外的所有模型的第三个重要特征。本研究建立的 ML 模型成功预测了 CAD/CAM RCB 的抗弯强度，并根据可用数据集确定了影响抗弯强度的有效成分。