法医病理学中的人工智能:用于估计死后间隔的多器官死后病理学
Guoshuai An1, Yu Gao1, Siyuan Cheng1
1School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi 030600, China; Shanxi Key Laboratory of Forensic Medicine, Jinzhong, Shanxi 030600, China.
Computer methods and programs in biomedicine
|July 12, 2025
概括
本研究介绍了死后病理学,使用组织学图像的深度学习来估计死后间隔. 多器官综合模型实现了高精度,为先进的法医分析铺平了道路.
科学领域:
- 法医病理学 法医病理学
- 数字病理学数字病理学
- 计算生物学是一种计算生物学.
背景情况:
- 准确的死后间隔估计在法医调查中至关重要.
- 使用全幻灯片图像的病理学是一种新的疾病诊断和预后方法.
- 死后病理学正在成为法医图像分析的一个关键子领域.
研究的目的:
- 开发一个三级层次的等级策略,使用病理学来进行死后组织图像分析.
- 创建一个多器官综合模型,用于准确的死后间隔估计.
- 为死后病理学领域建立基础方法.
主要方法:
- 分析了猪在各种死后时间的肝脏,脏和骨肌肉的全幻灯片图像.
- 深度学习模型 (DenseNet121,VGG16) 在质量控制和规范化后被训练在图像补丁上.
- 一个堆叠组合模型整合了器官特定预测,以获得最终的多器官个体级估计.
主要成果:
- 特定器官的深度学习模型实现了高精度:81.25% (肝脏),87.5% (脏) 和62.5% (肌肉).
- 综合多器官模型表现出强的性能,内部测试准确率为93.75%,外部验证准确率为87.5%.
- 对于特定的组织,DenseNet121和VGG16表现出优异的性能,形成了专门"网"的基础.
结论:
- 病理学和深度学习显示出对准确的死后间隔估计有很大的潜力.
- 开发的三级框架有效地整合了多器官数据,以改善法医分析.
- 全幻灯片成像提供了一种新的数据模式,推进了死后间隔确定策略.
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