比较几种机器学习工具辅助基于免疫荧光的抗中性粒细胞胞浆抗体检测的能力

Daniel Bertin, Pierre Bongrand, Nathalie Bardin
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引用次数: 0

摘要

人工智能和机器学习的成功激励着人们开发新的算法,以提高医疗诊断的快速性和可靠性。在此,我们比较了用于检测抗中性粒细胞胞浆抗体(一种重要的脉管炎标志物)的显微镜图像处理的不同策略:(i) 使用基本分类器方法(逻辑回归、k-近邻和决策树)处理从 137 种血清产生的免疫荧光图像中得出的定制指数。(iii) 基于神经网络的更复杂模型用于分析相同的数据集。用 Rand-type 精确度指数、kappa 指数和 ROC 曲线量化了区分阳性和阴性样本以及不同荧光模式的效率。结果表明,在有限的数据集上训练的基本模型可以区分阳性/阴性,其效率与人类进行的传统分析(0.84 kappascore)相当。要有效区分不同自身抗体产生的不同荧光模式,可能需要更广泛的数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of the capacity of several machine learning tools to assist immunofluorescence-based detection of anti-neutrophil cytoplasmic antibodies
The success of artificial intelligence and machine learning is an incentive to develop new algorithms to increase the rapidity and reliability of medical diagnosis. Here we compared different strategies aimed at processing microscope images used to detect anti-neutrophil cytoplasmic antibodies, an important vasculitis marker: (i) basic classifier methods (logistic regression, k-nearest neighbors and decision tree) were used to process custom-made indices derived from immunofluorescence images yielded by 137 sera. (ii) These methods were combined with dimensional reduction to analyze 1733 individual cell images. iii) More complex models based on neural networks were used to analyze the same dataset. The efficiency of discriminating between positive and negative samples and different fluorescence patterns was quantified with Rand-type accuracy index, kappa index and ROC curve. It is concluded that basic models trained on a limited dataset allowed positive/negative discrimination with an efficiency comparable to that obtained by conventional analysis performed by humans (0.84 kappa score). More extensive datasets may be required for efficient discrimination between different fluorescence patterns generated by different auto-antibody species.
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