基于深度学习的血液异常检测作为VEXAS综合征查工具
Cédric De Almeida Braga1, Maxence Bauvais2, Pierre Sujobert3
1Nantes Université, École Centrale Nantes, Nantes, France.
International journal of laboratory hematology
|September 14, 2024
概括
这项研究确定了周围血液多态核细胞中的特定失塑特征,以帮助诊断VEXAS综合征. 一个深度学习模型自动检测,改进了对UBA1基因突变的查.
科学领域:
- 血液学 血液学 血液学
- 计算生物学 计算生物学
- 遗传学 遗传学 是一个
背景情况:
- 维克萨斯综合征是最近 (2020) 描述的一种疾病,是由UBA1基因突变引起的,具有不同的临床和血液学特征.
- 目前VEXAS的诊断标准缺乏特异性,使得与其他炎症状况的早期区分具有挑战性.
- 在外周血液多态核细胞 (PMN) 中识别独特的失塑特征对于改善VEXAS查至关重要.
研究的目的:
- 为了确定外周血液多态核细胞 (PMN) 中特定的失塑特征,表明VEXAS综合征.
- 开发和验证一个深度学习算法,用于自动检测这些与VEXAS相关的PMN异常.
- 评估计算机辅助分析在指导VEXAS分子测试中的实用性.
主要方法:
- 编制了9514张注释的PMN图像的多中心数据集,包括VEXAS患者 (UBA1突变) 和对照组 (UBA1野生型髓质疏松和细胞缩).
- 进行了统计分析,以确定显著的PMN异常,将VEXAS与对照区分开来.
- 卷积神经网络 (CNN) 用于PMN特征的自动化,多标签分类.
主要成果:
- 与对照组相比,在VEXAS患者的PMN中观察到伪Pelger形式,核尖峰,真空和低粒度的显著差异.
- 自动检测系统实现了高性能,AUC从0.85到0.97不等,F1得分为0.70.
- 从模型中得出的VEXAS查得分,在预测UBA1突变状态时显示了0.82的灵敏度和0.71的特异性.
结论:
- 通过计算机辅助分析周围血液涂抹,可以显著帮助识别疑似VEXAS综合征患者.
- 开发的深度学习方法有助于血液学家指导诊断怀疑并优先进行分子测试.
- 这项技术通过实现有针对性的调查,提高了VEXAS诊断的效率.
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