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通过一个物体探测器模型重新审视识别核沟作为独特的核变化的实用性

Pedro R F Rende¹, Joel Machado Pires², Kátia Sakimi Nakadaira³

¹Bioregulation Department, Health and Science Institute, Federal University of Bahia, Salvador, Brazil.

Journal of pathology and translational medicine

|April 29, 2024

概括

这项研究引入了一种人工智能模型,用于识别甲状腺细针吸收图像中的核槽,以帮助诊断乳头甲状腺癌. 人工智能模型显示核评估的可行性,可能减少诊断变异性.

科学领域:

病理学病理学病理学
计算机科学计算机科学
医疗成像医学成像

背景情况:

核沟是乳头甲状腺癌 (PTC) 的特征.
人工智能 (AI) 显示出增强甲状腺细胞学诊断的前景.
开发用于常规诊断的AI工具是关键目标.

研究的目的:

开发一个监督的卷积神经网络 (CNN),用于识别核沟.
为了分析甲状腺细针吸收 (FNA) 的Diff-Quik染色全幻灯片图像 (WSI).
应用对象检测用于细胞病理学中的核结构分析.

主要方法:

使用了22个Diff-Quik染色细胞学幻灯片,证实了PTC诊断.
扫描幻灯片以创建WSI,提取感兴趣的区域,并注释核沟.
采用数据增强,并将数据集分为70%的培训和30%的验证组.

主要成果:

在甲状腺细胞病理学中开发了第一个使用对象检测用于核结构的AI模型.
处理了来自22个WSI的7,255张图像,注释了7,242个核槽.
在最好的模型中实现了67%的真正阳性,49.8%的灵敏度和43.1%的积极预测值.

结论:

人工智能模型成功预测了核沟结构,证明了细胞病理学的可行性.
基于人工智能的核沟识别可以减少观察者之间的变化和分析时间.
计算模型提供了一种可行的方法,通过核评估来改进甲状腺癌诊断.

关键词:

人工智能的人工智能是人工智能.细胞学细胞学精细针的吸收方式机器学习是机器学习.甲状腺腺是什么?甲状腺是什么?

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