使用人工智能预测牙科复合材料的性能
Karla Paniagua Rivera1, Kyumin Whang2, Krishna Joshi2
1Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX, 78249, USA.
medRxiv : the preprint server for health sciences
|October 17, 2024
概括
人工智能 (AI) 和机器学习 (ML) 模型可以预测牙复合材料的性能. 不同的ML模型准确地预测了诸如屈曲模量和收缩等特定结果,帮助新材料的开发.
科学领域:
- 材料科学 材料科学 材料科学
- 生物材料工程 生物材料工程
- 数据科学数据科学数据科学
背景情况:
- 牙复合材料需要提高性能和寿命.
- 加快新型牙科材料的市场转化至关重要.
- 预测建模可以优化复合开发.
研究的目的:
- 探索人工智能 (AI),特别是机器学习 (ML),从复合属性 (CA) 预测牙科复合材料性能结果 (PO).
- 评估各种ML模型的有效性,以预测牙科复合材料的关键性能指标.
主要方法:
- 从200多个出版物中编制了233个牙科复合样本的广泛数据集.
- 分析了17个复合属性 (CA) 和7个绩效结果 (PO).
- 评估了9个ML模型用于基于分类的PO预测,5个用于回归分析.
主要成果:
- K-最近邻居 (KNN) 在预测曲模量 (FlexMod) 中表现出色.
- 决策树模型对于屈曲强度 (FlexStr) 和体积收缩 (ShrinkV) 是最优的.
- 物流回归和支向量机 (SVM) 模型在收缩应力 (ShrinkStr) 中表现良好.
- 随机森林通过ROC AUC分析显示了FlexStr和ShrinkV的优异性能.
- 投票回归和决策树回归在特定的回归任务中表现出有效性.
- 确定了影响POs的关键复合物属性,包括TEGDMA,BisGMA,UDMA,固化深度,转化程度和填充物负载.
结论:
- 不同的ML模型在预测特定的牙复合材料性能结果方面表现出不同的强度.
- 一个全面的数据集和多模型方法对于训练强大的AI模型至关重要.
- 人工智能驱动的预测有助于优化复合材料的性能,并支持先进牙科材料的开发.
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