从中枢关节骨分类年龄使用30年值:基于图像分析的方法
Nela Ivković1, Željana Bašić1, Ivan Jerković1
1University Department of Forensic Sciences, University of Split, Split, Croatia.
PloS one
|November 22, 2024
概括
使用深度神经网络对中枢关节骨的图像分析可以区分年龄组. 这种方法在分类30岁以下和30岁以上的个体中达到80%以上的准确性.
科学领域:
- 法医人类学 法医人类学
- 医学成像分析 医学成像分析
- 医疗保健中的机器学习
背景情况:
- 准确的年龄估计在法医和临床环境中至关重要.
- 中间关节骨提供了与年龄相关的骨分析的潜力.
- 开发用于年龄分类的非侵入性方法是一个正在进行的研究领域.
研究的目的:
- 开发和验证基于图像分析的分类模型,使用中枢关节的特征来区分30岁以下和30岁以上的个体.
- 评估机器学习分类器对此任务的准确性和可靠性.
主要方法:
- 从多切片计算机断层扫描 (MSCT) 中提取2D中间关节骨图像.
- 使用预训练的深度神经网络 (Painters模型) 进行图像向量化.
- 主要组件分析 (PCA) 的应用,用于缩小维度和数据可视化.
- 使用支持矢量机 (SVM),物流回归 (LR) 和神经网络身份SGD (NNI-SGD) 进行分类,并进行5倍交叉验证和独立测试.
主要成果:
- 主要组件分析显示,关节骨分为不同的年龄类别 (30岁以下,40-55岁,80岁以上).
- 分类器达到>80%的准确性,整体准确性在交叉验证和测试集中为82.5%至92.5%.
- 后面的概率值为0.95,可以达到100%的分类准确度,尽管分类图像数量减少.
结论:
- 由预训练深度神经网络驱动的中枢关节骨的图像分析是年龄分类的可行方法.
- 开发的模型显示了区分年龄组的巨大潜力,特别是<30和>30二分法.
- 进一步的研究可以完善这些技术,以改善法医和临床年龄估计.
相关概念视频
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