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一个深度学习算法 (AIFORIA) 基于九个细胞类的骨髓吸附涂抹中的造血细胞的分类 - - 常规查的可行方法?

Leonie Saft^1,2, Emma Vaara³, Elin Ljung^4,5

¹Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden. leonie.saft@regionstockholm.se.

Journal of hematopathology

|March 29, 2025

概括

这项研究开发了一种人工智能算法,用于在骨髓细胞学中自动检测和分类造血细胞. 该工具具有很高的准确性和速度,为血液病提供可靠的查辅助.

科学领域:

血液学血液学血液学
计算病理学计算病理学
人工智能的人工智能

背景情况:

骨髓细胞学对于诊断血液病和评估细胞衰减至关重要.
数字图像分析中的人工智能 (AI) 具有提高诊断工作流程和准确性的潜力.

研究的目的:

开发一种深度学习算法,用于在数字化骨髓吸附涂抹中自动检测和分类造血细胞.
评估算法在临床实验室实施中的实用性.

主要方法:

使用AIFORIA创建平台开发了一个卷积神经网络算法,在数字化骨髓吸附涂抹上进行训练.
该算法在九个细胞类上进行了训练,并在单独的数据集上进行了验证,评估了整片图像 (WSI) 和感兴趣区域 (ROI) 的性能.

主要成果:

在训练过程中,人工智能模型实现了0.15%的总类错误,精度和灵敏度为99.9% (F1得分为99.2%).
外部验证显示了高性能,整体精度为96%和灵敏度为97% (F1得分为96%).
自动分类显示了ROI和快速执行时间的高相关性 (500个单元格<1s,WSI ≤260s).

结论:

基于云的深度学习算法提供了一个可靠和快速的选工具,用于在骨髓涂抹中检测和分类造血细胞.
人工智能工具可以有效地在临床实验室环境中补充细胞形态学,用于血液病诊断.

关键词:

骨髓骨髓骨髓骨髓骨髓深度学习算法深度学习算法造血细胞是造血细胞的组成部分.

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