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基于图像的第二意见用于血型鉴定

Sergey Korchagin^1,2, Ekaterina Zaychenkova^1,2, Egor Ershov^1,2

¹Institute for Information Transmission Problems, Moscow, Russia.

Health information science and systems

|April 5, 2024

概括

这项研究引入了一种新的AI方法,以提高手动血型测定准确度. 神经网络达到98.4%的准确性,与血液分型的人类分析相比,大大减少了错误.

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
临床实验室科学临床实验室科学

背景情况:

使用血清板进行手动血型鉴定是一种常见但容易出错的诊断方法.
该过程涉及视觉聚合评估,易受人为错误和耗时.
提高血型鉴定的准确性和效率对于患者安全和医疗保健系统至关重要.

研究的目的:

开发和评估一种基于神经系统的新型分类方法,用于自动化血型分析.
为了提高手动血型鉴定中的凝结分析步骤的质量和性能.
为实验室助理在血液分型程序中提供可靠的第二意见.

主要方法:

收集了3139个井图像的新数据集,其中包括地面真相和专家评估.
最先进的神经网络架构用于图像分类.
由于任务的低语义性质,也研究了浅层神经网络.

主要成果:

提出的基于神经系统的方法实现了98.4%的整体准确性.
人工智能解决方案的错误数量比普通人类专家的错误数量少2.75倍.
性能与最好的人类专家相当,浅层网络显示出与先进模型相似的准确性.

结论:

基于神经系统的分类方法显著提高了准确性,并减少了手动血型分类中的错误.
人工智能为血液分型的分析步骤提供了可行和高效的解决方案,补充了人类的专业知识.
开发的系统可以集成到实验室系统中,以获得更快,更可靠的结果.

关键词:

聚合方式聚合方式聚合方式血液类型的测试.分类分类分类分类.深度学习是一种深度学习.图像分析图像分析神经网络的神经网络的神经网络

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