使用ML算法和CLAHE技术在不同的临床照明下,在口腔内图像中预测牙颜色:一个In-Vivo研究
Esra Efitli1, Abdullah Ammar Karcioglu2, Alper Ozdogan3
1Department of Software Engineering, Atatürk University, Erzurum, Turkey.
Lasers in medical science
|October 6, 2025
概括
这项研究开发了一种机器学习模型,用于准确预测牙颜色,克服牙科治疗中的主观性和照明问题. 该模型达到97%的准确性,为牙医提供可靠的工具.
科学领域:
- 牙科材料科学 牙科材料科学
- 计算机视觉在牙科中的应用
- 机器学习应用程序 机器学习应用程序
背景情况:
- 牙颜色的选择是主观的,并受到照明的影响,影响假牙治疗.
- 目前用于牙颜色分析的仪器方法昂贵,缺乏标准化.
- 临床照明条件的变化对准确的牙阴影匹配构成了挑战.
研究的目的:
- 使用机器学习 (ML) 开发可靠和一致的牙颜色预测模型.
- 在不同的临床照明条件下确保准确的牙颜色预测.
- 提供一个实用,低成本的工具,用于假牙牙科的决策.
主要方法:
- 在五个不同的临床光源下收集前牙的体内口腔图像.
- 应用对比限度自适应直方图平衡 (CLAHE) 来增强图像和减少光.
- 使用数据增强用于类不平衡,并训练了五个ML算法 (RF,KNN,后勤回归,决策树,XGBoost).
主要成果:
- 随机森林 (RF) 和XGBoost在牙颜色预测方面实现了最高的性能,准确率为97%.
- 该模型在可变照明场景中表现出强度和准确性.
- 提出的方法在光独立的牙颜色预测中被证明是有效的.
结论:
- 机器学习算法与图像增强技术相结合,提供准确的,光独立的牙颜色预测.
- 开发的模型为牙科的临床决策提供了实用且具有成本效益的解决方案.
- 这种方法提高了牙假体颜色匹配过程的标准化和效率.
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