机器学习回归模型用于对不同牙颜色背景的CAD-CAM材料的颜色预测
Bruno Arruda Mascaro1, Rafael Vázquez Conejo2, Maria Tejada-Casado2
1Department of Dental Materials and Prosthodontics, São Paulo State University (UNESP), School of Dentistry, Araraquara 14801-903, São Paulo, Brazil.
Journal of dentistry
|July 27, 2025
概括
一种用于CAD-CAM材料的新色彩预测模型准确地预测了不同厚度和背景的修复阴影. 这种机器学习方法提高了创造美学间接修复的可预测性和效率.
科学领域:
- 牙科材料科学 牙科材料科学
- 颜色科学 颜色科学
- 机器学习在牙科中的应用
背景情况:
- 精确的颜色匹配对于美观的牙科修复至关重要.
- 预测CAD-CAM材料的最终颜色可能因材料特性,厚度和背景阴影而具有挑战性.
研究的目的:
- 开发和验证CAD-CAM材料的颜色预测模型.
- 为了评估模型在各种材料厚度和牙颜色背景的准确性.
主要方法:
- 四种CAD-CAM材料 (Lava Ultimate,Grandio Blocs,VITA Enamic,Vita Mark II) 在三个厚度 (0.5,1.0,1.5毫米) 中进行制造.
- 在CIE-L*a*b*颜色坐标测量时,使用光谱辐射计对各种背景进行测量.
- 部分最小平方回归 (PLS) 和留出一个的交叉验证 (LOOCV) 用于构建和测试预测模型.
主要成果:
- 开发的模型在所有测试条件下实现了可接受的颜色差异 (ΔE00 <1.81).
- 当根据材料和厚度分析数据时,获得了不可察觉的颜色差异 (ΔE00 < 0.80).
- 随着材料厚度的增加,预测准确性得到了改善,其中"a*"坐标显示了最佳模型匹配 (RMSE = 0.14).
结论:
- 对LOOCV和PLS回归的整合为CAD-CAM修复提供了一个稳定和可重复的预测色彩模型.
- 这种模型在临床上适用于预测不同牙色调上的不同厚度的修复的颜色.
- 机器学习模型为提高美学间接修复的可预测性和效率提供了一个有希望的工具.
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