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拉米内特-维纳工作流程中的人工智能:准确性,效率和美学可预测性的系统审查和元分析

Osama Hajeer¹, Amal Hasan¹

¹Department of Fixed Prosthodontics, Faculty of Dentistry, University of Aleppo, Aleppo, Syrian Arab Republic.

Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.]

|January 6, 2026

概括

人工智能 (AI) 在牙科层工艺中显示出有希望的准确性,提高了颜色预测和边缘线检测等任务的效率. 对于这些人工智能技术的广泛临床使用,需要进一步的研究.

科学领域:

牙科技术牙科技术
人工智能在牙科中的应用
修复性和美学牙科修复性和美学牙科

背景情况:

人工智能 (AI) 在修复性和美学牙科的整合正在不断增长,但其在板涂层工作流程中的可靠性需要彻底评估.
目前对人工智能在板板制造和放置的所有阶段的性能的理解是有限的.

研究的目的:

系统地评估AI系统的精度,效率和审美可预测性,这些都应在层层涂层工作流程中进行.
评估AI应用在颜色预测,边缘线检测,CAD自动化和牙贴片的美学模拟方面.

主要方法:

在五个主要的电子数据库中,按照PRISMA 2020指南进行了系统审查.
包括专注于人工智能/机器学习的研究在任何层贴片工作流程步骤中,报告定量结果 (体外,内,临床).
使用了元分析,偏差风险评估 (PROBAST-AI,QUADAS-AI,ROBINS-I) 和证据的分级确定性.

主要成果:

人工智能颜色预测实现了高准确度 (97.9%),颜色偏差最小 (ΔE00=1.85),低于临床值.
自动边缘线检测显示出高准确度 (Dice=0.947) 和精度 (72μm偏差).
人工智能辅助的CAD显著减少了设计时间 (p<0.001),而不影响形态;美学预测显示了中等至高的一致性,尽管患者的偏好有所不同.

结论:

人工智能应用显然提高了效率,并保持了临床上可接受的精度,在层-薄膜工作流程.
适度的证据支持人工智能在边缘线准确性和设计效率方面;对颜色和美学预测的证据较低.
较大的临床数据集,外部验证和标准化报告对于常规AI在层层程序中的实施至关重要.

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