采用转移学习的3D自动化面部微笑吸引力评估在整形手术前后:一项初步研究
Wen-Chung Chiang1, Hui-Ling Chen2, Hsiu-Hsia Lin3
1Department of Intelligent Technology and Application, Hungkuang University, Taichung, Taiwan.
概括
整形手术 (OGS) 后,面部微笑的吸引力显著改善,观察到25%的增强. 使用3D面部轮的机器学习模型提供了客观的定量评估,在OGS之前和之后.
科学领域:
- 审美学 在审美学方面
- 计算机科学 计算机科学
- 医疗成像医学成像
背景情况:
- 微笑美学显著影响面部吸引力,使微笑分析在牙科和外科手术中至关重要.
- 对微笑吸引力的客观评价对于规划和评估整形手术 (OGS) 的结果至关重要.
研究的目的:
- 开发和验证一种机器学习模型,用于客观,定量地评估OGS前后面部微笑吸引力.
- 为了评估基于网络的自动微笑吸引力评估系统的有效性.
主要方法:
- 这是一项对135名接受OGS治疗的患者进行的回顾性队列研究.
- 使用转移学习 (TL) 模型与卷积神经网络 (CNN) 在3D面部轮特征上.
- 在手术前后使用3dMDTM面部系统捕获了3D面部照片.
主要成果:
- 在手术后,面部微笑吸引力得分从2.62增加到3.27,表明25%的增强.
- 机器学习模型在微笑吸引力方面显著改善.
- 一个用户友好的基于网络的系统促进了快速评估和医生与患者的沟通.
结论:
- 本研究介绍了使用3D轮特征和机器学习评估面部微笑吸引力之前和之后的第一个自动化,客观和定量方法.
- 开发的TL-CNN模型和基于网络的系统显示出在OGS结果评估和患者沟通中临床应用的前景.
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