在一个亚洲女性群体中,通过可解释的机器学习辅助方法解读中脸部缩或突起的尾形态机制
Kang Wang1, Yi-Hao Qiao2, Zhou-Jie Gong3
1State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China; Department of Plastic and Burn Surgery, West China Hospital, Sichuan University, Chengdu, China; Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
概括
机器学习方法准确诊断中部面部形态,分类平衡的面部,中部面部缩或大突起. 该系统通过识别骨和牙异常,有助于面部美学,有助于治疗规划.
科学领域:
- 头骨面部形态学 头骨面部形态学
- 医学成像医学成像
- 机器学习在医疗保健中的应用
背景情况:
- 传统的以测量为基础的诊断对于复杂的面结构是不够的.
- 需要先进的评估系统,特别是机器学习辅助的方法,用于中界面分析.
- 准确的诊断对于正牙科和整形外科的有效治疗计划至关重要.
研究的目的:
- 开发和验证用于诊断亚洲女性中部面部形态的机器学习模型.
- 为了将面部外观分为平衡的,中部面部缩或大突出类型.
- 为了确定预测面部中部异常的关键面特征.
主要方法:
- 利用1293名亚洲女性的综合测量系统,选择了424名女性进行分析.
- 开发和训练机器学习模型,包括XGBoost,以分类中面类型.
- 使用特征重要性和外部测试集评估模型性能,评估诊断准确性.
主要成果:
- XGBoost模型在诊断中部面部形态方面表现出卓越的表现.
- 从鼻下到脑和从大到头骨底部的前后关系被确定为关键的诊断特征.
- 这些模型有效地诊断了中部面部类型,即使没有面部角度测量,也显示了强度.
结论:
- 机器学习为诊断复杂的中脸形态提供了一个强大的,可解释的工具.
- 开发的模型可以准确地区分平衡的脸部,中脸部缩和大突出.
- 这种方法有助于识别面部美学缺陷的骨和牙原因.
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