在儿童,青少年和年轻成年人中,深度学习用于法医年龄估计,使用骨科影像
Rahel Mara Koch1, Hans-Joachim Mentzel2, Andreas Heinrich3
1Department of Radiology, Jena University Hospital-Friedrich Schiller University, Am Klinikum 1, 07747, Jena, Germany.
European radiology
|January 25, 2025
概括
卷积神经网络 (CNN) 增强了OPG的法医年龄估计,在1-2.5年内达到高精度. 这种深度学习方法为传统的法医年龄诊断提供了可靠,客观的补充.
科学领域:
- 法医科学 法医科学 法医科学
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 法医年龄估计对于法律和道德原因至关重要.
- 骨架影像 (OPG) 通常用于牙科年龄评估.
- 传统的方法可能是耗时和主观的.
研究的目的:
- 用OPGs评估定制卷积神经网络 (CNN) 的准确性,用于法医年龄估计.
- 评估1至25岁个体年龄预测的改善情况.
- 将CNN的表现与专家估计进行比较.
主要方法:
- 一个定制的CNN在13,766名个人 (1至<25岁) 的21,814个OPG的大型数据集上接受了培训.
- 美国有线电视新闻网接受了广泛的培训和验证.
- 该模型在一个独立的测试集上进行了评估,并应用于真实的法医病例.
主要成果:
- 美国有线电视新闻网 (CNN) 的平均绝对误差 (MAE) 为0.93年,平均符号误差 (MSE) 为-0.06年.
- 63%的预测在1年内准确,95%在2.5年内准确.
- 美国有线电视新闻网的表现与专家法医年龄估计相当.
结论:
- 在大型,多样化的数据集上训练的定制CNN显著提高了法医年龄估计的准确性.
- 这种深度学习方法为标准方法提供了可靠,强大和客观的补充.
- 基于CNN的年龄估计为法医专家提供了宝贵的支持.
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