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Muscles for Facial Expressions01:14

Muscles for Facial Expressions

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The craniofacial muscles are a collection of approximately 20 thin skeletal muscles situated beneath the skin of the face and scalp. These muscles, primarily responsible for the vast array of human facial expressions, originate from the bones or fibrous structures of the skull and extend outwards to connect with the skin. While most skeletal muscles in the body are enveloped in thick fascia, facial muscles generally have a more delicate fascial covering, with the buccinator muscle being a...
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Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
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使用最新一代人工智能方法分析Acromegaly面部变化:AcroFace系统

Hatem A Rashwan1, Montserrat Marqués-Pamies2, Sabina Ruiz3

  • 1Department of Computer Engineering and Mathematics, University of Rovira i Virgili, Tarragona, Spain.

Pituitary
|April 21, 2025
PubMed
概括

该AcroFace系统使用人工智能对面部照片进行分析,以早期检测巨. 这种人工智能工具在识别壮病特征方面表现出高准确度,有助于在人口层面进行查.

关键词:
壮成长是一种壮成长.宏观壮观的检测检测人工智能的人工智能是人工智能.面部分析 面部分析面部的变化 面部的变化

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科学领域:

  • 人工智能在医学中的应用
  • 医学成像分析 医学成像分析
  • 内分泌学 在内分泌学.

背景情况:

  • 巨症是一种罕见的内分泌疾病,其特点是生长激素的过度产生.
  • 早期检测壮症对于及时干预和管理并发症至关重要.
  • 面部特征是已知的壮症指标,但需要客观和可扩展的检测方法.

研究的目的:

  • 开发和评估AcroFace系统,这是一款基于人工智能的工具,用于使用面部照片早期检测巨.
  • 探索将视觉/纹理特征与来自面部图像的几何信息相结合的有效性,以检测壮症.

主要方法:

  • 开发了AcroFace系统,用于几何特征集成支持向量机 (SVM) 和用于视觉特征的卷积神经网络 (CNN).
  • 通过使用专家注释的面部图像来训练各种CNN模型 (ResNet-50,VGG-16,MobileNet,Inception V3,DensNet121,Xception).
  • 优化特征提取和分类策略,以准确检测巨病.

主要成果:

  • 与支持向量回归 (SVR) 结合的ResNet-50模型实现了最高的性能,精度值为75% (δ1) 和89% (δ3).
  • 与单独的几何特征相比,视觉特征表现出更高的精度.
  • 验证队列实现了高性能指标:0.90精度,0.93准确度,0.92F1-Score,0.93灵敏度和0.93特异性.

结论:

  • 该AcroFace系统在区分与巨相关的面部特征方面表现出强的性能.
  • 人工智能系统具有作为人口层面查工具的潜力,用于早期检测壮病.
  • 使用人工智能的面部照片分析提供了一种有前途的非侵入性方法,用于巨病查.