使用深度学习评估VITA基于阴影的牙颜色类别
Jin-Sun Jeong1, Kyeong-Seop Kim2, Yu Gu3
1School of Nursing and Rehabilitation, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China.
Scientific reports
|November 17, 2025
概括
一个新的深度学习模型准确地评估牙阴影,为主观视觉方法提供可靠的替代方案. 这种人工智能方法尽量减少主观性,在牙美学评估的精度上超过了经验丰富的牙医.
科学领域:
- 牙科 牙科是指牙科的专业.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 越来越多的患者对牙美学的需求需要客观的牙阴影评估.
- 使用商业指南进行传统的视觉阴影评估是主观的,缺乏可靠性.
- 在临床牙科实践中需要先进的客观方法.
研究的目的:
- 开发和验证深度学习 (DL) 模型,用于客观地评估牙色调.
- 为了比较DL模型与经验丰富的牙医实践者的准确性.
- 提高牙科审美评估的可靠性和效率.
主要方法:
- 分析了70名参与者的高分辨率口内图像.
- 包括修改的CNN架构 (ResNet) 在内的深度学习技术用于牙阴影检测,细分和分类.
- 性能指标 (准确性,精度,回忆,F1分数) 和麦克纳马的测试被用于评估.
主要成果:
- 基于ResNet的深度学习模型在牙色调分类方面表现最佳.
- 与经验丰富的牙医相比,DL模型实现了统计学上显著的更高准确性.
- 该模型被证明是牙阴影评估的可靠和有效工具.
结论:
- 深度学习为牙科中牙颜色评估提供了可靠和客观的替代方案.
- 开发的DL模型尽量减少了传统视觉方法固有的主观性.
- 人工智能驱动的牙阴影评估可以超过经验丰富的临床医生,改善牙美学结果.
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