人工智能有助于在糖尿病病症患者中识别和病理分类淋巴细胞病变
Qunjuan Lei1, Xiaoshuai Hou2, Xumeng Liu1
1National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, 305 East Zhongshan Road, Nanjing, 210009, China.
Journal of translational medicine
|April 29, 2024
概括
卷积神经网络 (CNN) 可以自动量化糖尿病脏病 (DN) 中的球损伤,有助于准确的病理分类和预后预测.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 计算病理学计算病理学
- 医学成像分析 医学成像分析
背景情况:
- 糖尿病脏病 (DN) 的特点是球膜病变,这对于病理学分类至关重要.
- 目前对这些特征的手动量化是耗时且半量化的.
- 迫切需要在DNA中自动定量质形态特征.
研究的目的:
- 开发和验证卷积神经网络 (CNN),用于在DN中自动识别和分类球形态特征.
- 分析这些数字特征与DN病理分类和患者预后的关联.
主要方法:
- 一系列CNN模型被设计用于识别和分类球体形态特征.
- 这些模型在DN患者数据上进行了训练和测试.
- 分析了CNN衍生特征,病理分类和临床结果之间的关联.
主要成果:
- 美国有线电视新闻 (CNN) 的模型在全球淋巴结核硬化 (0.928) 和金梅尔斯蒂尔-威尔逊病变 (0.953) 中获得了高F1分数.
- 能够精确量化中膜区域 (Dice 0.870) 和球膜内在细胞 (F1 > 0.839).
- 基于CNN的分类显示了与病理学家的中度一致 (kappa=0.624) 和功能等同的预测性表现.
结论:
- 基于CNN的模型在协助DN患者的淋巴细胞病变的识别和病理分类方面表现有前途.
- 自动化分析可以提供对关键DN特征的客观和有效量化.
- 这项技术有可能提高DN的诊断准确性和患者管理.
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