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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.

|April 21, 2025

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.

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.

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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