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相关概念视频

Classification of Leukocytes01:30

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

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Comprehensive Protocol to Sample and Process Bone Marrow for Measuring Measurable Residual Disease and Leukemic Stem Cells in Acute Myeloid Leukemia
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一个数字工具支持病理学实践和识别白细胞.

Dumitru-Cristian Apostol1, Antonela-Maria Chiuzbăian2, Mihaela Crisan-Vida1

  • 1Faculty of Automation and Computers, University Politehnica Timişoara, Romania.

Studies in health technology and informatics
|August 23, 2024
PubMed
概括

这项研究引入了一种自动化的数字病理学工具,用于精确的白细胞计数和从图像中确定质量. 该系统实现了高准确度,帮助病理学家预测疾病结果,使用最小的手动输入.

关键词:
人工智能的人工智能智能模型是一个智能模型.病理学 病理学 病理学

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科学领域:

  • 数字病理学数字病理学
  • 计算生物学 计算生物学
  • 医学图像分析 医学图像分析

背景情况:

  • 病理学工作流通常需要手动细胞计数和分析,这耗时且容易引起观察者之间的变化.
  • 精确量化白细胞及其特征对于诊断和预测疾病结果至关重要.
  • 现有的数字病理学工具可能缺乏全面的可追溯性或需要严重的病理学家干预.

研究的目的:

  • 开发一个集成的数字病理学解决方案,用于自动化白细胞分析.
  • 从病理图像直接提供白细胞计数和细胞质量的准确量化.
  • 确定白细胞和潜在疾病结果之间的可追溯性,促进概念验证 (PoC) 或原型.

主要方法:

  • 开发一个集成数据处理工具,预定义的分析模型和数字病理学接口的全集包.
  • 在大约20,000张图像的数据集上训练机器学习模型,用于白细胞识别和量化.
  • 实现视觉脚本和直观的界面,以减少病理学分析的学习曲线.

主要成果:

  • 开发的模型在白细胞分析中实现了约85%的整体准确性.
  • 该系统显示,确定的感兴趣领域的真正预测率约为82%.
  • 模型正确地识别了病理学家标记的约89%的阳性病例,在负面病例中,假阳性率为6%.

结论:

  • 自动化数字病理学工具有效地从图像中量化白细胞及其质量,病理学家的干预最小.
  • 该系统在识别和分类白细胞区域方面提供了高精度,支持诊断和预后评估.
  • 综合平台提高了病理学分析的效率,并为临床决策提供了宝贵的可追溯性.