可解释的人工智能系统使用卷积神经网络来设计可拆卸部分假牙框架的主要连接器
Yajie Li1, Yuka Inamochi1, Tatsuhiko Anzai2
1Department of Masticatory Function and Health Science, Institute of Science Tokyo, Tokyo, Japan.
Journal of prosthodontic research
|January 25, 2026
概括
使用卷积神经网络 (CNN) 的AI系统可以准确地设计可移动部分假牙 (RPD) 的主要连接器. 这种人工智能方法提高了RPD框架设计的可解释性和一致性.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在牙科中的应用
- 牙修复 牙修复 牙修复 牙修复 牙修复
背景情况:
- 可拆卸部分假牙 (RPD) 框架设计,特别是主要连接器,需要专门的专业知识.
- 传统的设计方法可以根据临床医生的技能和经验表现出变化.
- 自动化设计系统为提高一致性和效率提供了潜力.
研究的目的:
- 开发一种利用卷积神经网络 (CNN) 的人工智能 (AI) 系统,用于RPD主要连接器的自动设计.
- 通过使用梯度加权类激活映射 (Grad-CAM) 提高人工智能驱动的RPD设计的可解释性.
- 提供一个工具,尽量减少RPD框架中的设计变化.
主要方法:
- 开发了10个连续的CNN模型,这些模型是根据1000个RPD和255个牙科变量的数据进行训练的.
- 模型预测了假牙类型,设计侧面性,主要连接器的存在,以及大和下的特定连接器类型.
- 使用精度,灵敏度,特异性,预测值和F1分数来评估模型性能;用于可视化使用Grad-CAM.
主要成果:
- 在预测主要连接器类型方面,CNN模型取得了很高的准确性 (例如,大巴巴条/带在93.0%,下巴舌条/板在87.3%).
- 格拉德-CAM热图确定了关键决定因素,如牙周状况,牙缺陷和闭合关系.
- 人工智能系统证明了对RPD主要连接器分类的有效预测.
结论:
- 一个基于人工智能的系统是可行的预测RPD主要连接器类型的高性能.
- 人工智能系统可以通过减少设计变化和提高一致性来支持牙医.
- Grad-CAM可视化提高了可解释性,帮助临床医生和患者理解设计逻辑,可能提高培训和效率.
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