基于多输入3D卷积神经网络的面部美学3D评估模型用于整形外科手术
Qingchuan Ma1,2, Etsuko Kobayashi3, Siao Jin3
1Department of Oral-Maxillofacial Surgery and Orthodontics, The University of Tokyo Hospital, Tokyo, Japan.
概括
一个新的深度学习模型,DeepBeauty3D,使用3DCT扫描来自动得分面部美学在整形手术. 这种方法比传统方法提供了更快,更客观的评估.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 整形外科 整形外科 整形外科
背景情况:
- 定量化面部美学评估在整形手术中至关重要,但耗时.
- 现有的二维模型缺乏临床影响,通常用于娱乐.
- 在手术规划中需要客观,高效的审美评估工具.
研究的目的:
- 开发和验证基于深度学习的3D模型,用于定量面部美学评估.
- 使用患者特定的解剖数据自动化审美评分.
- 提高整形外科美学评估的效率和客观性.
主要方法:
- 一个深度学习模型,DeepBeauty3D,在133名患者的CT图像上被设计和训练.
- 一个定制的预处理模块提取了骨,软组织和个人物理数据.
- 采用3输入2输出卷积神经网络 (CNN) 架构进行得分预测.
主要成果:
- 该模型在预测骨 (4.62%) 和软组织 (2.00%) 的审美得分方面取得了很高的准确性.
- 从原始CT图像进行自动评分快速完成,大约在11秒内完成.
- 该模型在真实临床数据上表现出强大的性能.
结论:
- DeepBeauty3D提供了一个端到端的解决方案,用于使用3D CNNs进行定量面部美学评估.
- 该模型同时考虑多个解剖学因素进行全面评估.
- 这项技术显示出降低外科手术工作量和调整外科医生-患者美学视角的潜力.
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