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Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
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一个高效的急性淋巴细胞白血病查框架,基于多模式深度神经网络.

Qiuming Wang1, Tao Huang2, Xiaojuan Luo2

  • 1Computer Vision Institute, College of Computer Science and Software, Shenzhen University, China.

International journal of laboratory hematology
|January 15, 2025
PubMed
概括

结合白细胞 (WBC) 散射图和全血细胞计 (CBC) 数据的新型深度神经网络框架显著提高了早期急性淋巴细胞白血病 (ALL) 查准确度. 这种方法提高了诊断的敏感性和特异性,有助于及时治疗儿科患者.

关键词:
急性淋巴细胞白血病 (ALL)深度神经网络是一个神经网络.早期查 早期查多模式学习是多模式学习.白血细胞计数 (WBC) 分散图.

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科学领域:

  • 医学诊断 医学诊断 医学诊断
  • 医疗保健中的人工智能
  • 血液学 血液学 血液学

背景情况:

  • 急性淋巴细胞白血病 (ALL) 是儿童癌症死亡的主要原因.
  • 及时和准确的ALL诊断对于有效的治疗和改善生存率至关重要.

研究的目的:

  • 开发和验证一个多模态深度神经网络,用于早期和高效的ALL查.
  • 为了利用白细胞 (WBC) 分散图和全血细胞 (CBC) 数据来提高ALL检测.

主要方法:

  • 提出了一个深度学习框架,集成WBC散射图和CBC数据.
  • 该模型在包括ALL,传染性单核病 (IM) 和健康对照 (HC) 患者在内的数据集上进行了训练和验证.

主要成果:

  • 综合方法在交叉验证中获得了98.43%的准确性,在外部验证中获得了96.67%的敏感性.
  • 该模型的曲线下的面积 (AUC) 超过了0.99,超过了人类专家的性能.

结论:

  • 这个框架代表了WBC散射图和CBC数据用于ALL查的新整合.
  • 该方法表现出高灵敏度和特异性,有望改善早期ALL诊断,并减少医疗人员的工作量.