确定皮肤年龄的机器学习方法:系统审查
Eric McMullen1, Rokhshid Aflaki2, Pranav Jignesh Khatri2
1Division of Dermatology, Department of Medicine, University of Toronto, Toronto, ON, Canada.
Journal of tissue viability
|April 2, 2025
概括
机器学习使用面部图像和生物标志物准确估计皮肤衰老,但为了更好的概括性,需要不同的数据集. 本综述强调了年龄预测模型当前的准确性和局限性.
科学领域:
- 皮肤病学 皮肤病学
- 计算机科学 计算机科学
- 生物技术是生物技术.
背景情况:
- 皮肤衰老是一个复杂的过程,受内在和外在因素的影响.
- 从皮肤外观准确地测定年龄是越来越多的研究领域.
研究的目的:
- 在确定皮肤衰老方面系统地审查机器学习应用.
- 评估当前文献的准确性,局限性和研究缺陷.
主要方法:
- 在OVID Embase,OVID Medline,IEEE Xplore和ACM数字图书馆的系统文献搜索.
- 选了1467篇文章,其中27篇被列入最终审查.
- 分析机器学习模型性能和方法.
主要成果:
- 机器学习模型实现了准确度,平均绝对误差在2.30至8.16年之间.
- 全面图像分析是最常用的方法;生物标志物分析 (甲基组,蛋白质组) 也被使用.
- 动态面部表情改善了年龄估计的准确性 (MAE 3.74);共聚焦显微镜显示高达85%的准确性.
- 在许多研究中注意到存在偏差的高风险,这通常是由于样本大小小.
结论:
- 机器学习显示了准确估计皮肤年龄的前景.
- 未来的研究需要更多多样化的数据集 (种族,变量) 来提高概括性.
- 解决像小样本大小这样的局限性对于稳健的模型开发至关重要.
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