A Machine Learning Approach Towards Standardizing Microscopic Agglutination Test for Diagnosis of Leptospirosis
A Machine Learning Approach Towards Standardizing Microscopic Agglutination Test for Diagnosis of Leptospirosis
复制标题
一种标准化显微凝集试验诊断钩端螺旋体病的机器学习方法
DOI：
10.1101/2020.12.08.410712
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Fujii Jun
中科院分区：
文献类型：
--
作者：
Oyamada Yuji;Ozuru Ryo;Masuzawa Toshiyuki;Miyahara Satoshi;Nikaido Yasuhiko;Obata Fumiko;Saito Mitsumasa;Villanueva Sharon Yvette Angelina M.;Fujii Jun
钩端螺旋体病是一种由致病菌钩端螺旋体引起的人畜共患病。显微凝集试验（MAT）被广泛用作钩端螺旋体病诊断的金标准。该方法将稀释后的患者血清与血清型确定的钩端螺旋体混合，在暗场显微镜下检测是否存在聚集，计算抗体滴度。当前MAT方法的问题在于：1)每个样本需要检查许多样本；2)需要将污染物与真实聚集物区分开来，以准确识别阳性。因此，提高效率和准确性是改进MAT的关键。通过自动化决策过程，可以同时实现效率和标准化准确性。在本研究中，我们使用机器学习方法构建了MAT的自动识别算法来确定微观图像中的凝集。该机器分别从感染钩端螺旋体（阳性）和未感染（阴性）的仓鼠血清中创建的316个阳性和230个阴性MAT图像中学习了这些特征。除了获取的原始图像外，还将小波变换后的图像作为特征。我们使用支持向量机（SVM）作为建议的决策方法。我们用210张阳性图像和154张阴性图像验证了训练好的支持向量机。当从原始图像或小波变换图像中获取特征时，所有的阴性图像都被误判为阳性，分类性能很低，灵敏度为1，特异性为0。而以小波系数直方图作为特征时，其灵敏度为0.99，特异度为0.99，性能有很大提高。我们证实了目前的算法判断MAT图像中的正或负凝集，并为MAT过程的自动化提供了进一步的可能性。
Leptospirosis is a zoonosis caused by the pathogenic bacteriumLeptospira. The Microscopic Agglutination Test (MAT) is widely used as the gold standard for diagnosis of leptospirosis. In this method, diluted patient serum is mixed with serotype-determined Leptospires, and the presence or absence of aggregation is determined under a dark-field microscope to calculate the antibody titer. Problems of the current MAT method are 1) a requirement of examining many specimens per sample, and 2) a need of distinguishing contaminants from true aggregates to accurately identify positivity. Therefore, increasing efficiency and accuracy are the key to refine MAT. It is possible to achieve efficiency and standardize accuracy at the same time by automating the decision-making process. In this study, we built an automatic identification algorithm of MAT using a machine learning method to determine agglutination within microscopic images. The machine learned the features from 316 positive and 230 negative MAT images created with sera ofLeptospira-infected (positive) and non-infected (negative) hamsters, respectively. In addition to the acquired original images, wavelet-transformed images were also considered as features. We utilized a support vector machine (SVM) as a proposed decision method. We validated the trained SVMs with 210 positive and 154 negative images. When the features were obtained from original or wavelet-transformed images, all negative images were misjudged as positive, and the classification performance was very low with sensitivity of 1 and specificity of 0. In contrast, when the histograms of wavelet coefficients were used as features, the performance was greatly improved with sensitivity of 0.99 and specificity of 0.99. We confirmed that the current algorithm judges the positive or negative of agglutinations in MAT images and gives the further possibility of automatizing MAT procedure.