Abstract：
BACKGROUND: Hemoglobin (Hb) is an important protein in red blood cells and a crucial diagnostic indicator of diseases, e.g., diabetes, thalassemia, and anemia. However, there is a rare report on methods for the simultaneous screening of diabetes, anemia, and thalassemia. Isoelectric focusing (IEF) is a common separative tool for the separation and analysis of Hb. However, the current analysis of IEF images is time-consuming and cannot be used for simultaneous screening. Therefore, an artificial intelligence (AI) of IEF image recognition is desirable for accurate, sensitive, and low-cost screening.
RESULTS: Herein, we proposed a novel comprehensive method based on microstrip isoelectric focusing (mIEF) for detecting the relative content of Hb species. There was a good coincidence between the quantitation of Hb via a conventional automated hematology analyzer and the one via mIEF with R2 = 0.9898. Nevertheless, our results showed that the accuracy of disease diagnosis based on the quantification of Hb species alone is as low as 69.33 %, especially for the simultaneous screening of multiple diseases of diabetes, anemia, alpha-thalassemia, and beta-thalassemia. Therefore, we introduced a ResNet1D-based diagnosis model for the improvement of screening accuracy of multiple diseases. The results showed that the proposed model could achieve a high accuracy of more than 90 % and a good sensitivity of more than 96 % for each disease, indicating the overwhelming advantage of the mIEF method combined with deep learning in contrast to the pure mIEF method.
CONCLUSIONS: Overall, the presented method of mIEF with deep learning enabled, for the first time, the absolute quantitative detection of Hb, relative quantitation of Hb species, and simultaneous screening of diabetes, anemia, alpha-thalassemia, and beta-thalassemia. The AI-based diagnosis assistant system combined with mIEF, we believe, will help doctors and specialists perform fast and precise disease screening in the future.
RESULTS: Herein, we proposed a novel comprehensive method based on microstrip isoelectric focusing (mIEF) for detecting the relative content of Hb species. There was a good coincidence between the quantitation of Hb via a conventional automated hematology analyzer and the one via mIEF with R2 = 0.9898. Nevertheless, our results showed that the accuracy of disease diagnosis based on the quantification of Hb species alone is as low as 69.33 %, especially for the simultaneous screening of multiple diseases of diabetes, anemia, alpha-thalassemia, and beta-thalassemia. Therefore, we introduced a ResNet1D-based diagnosis model for the improvement of screening accuracy of multiple diseases. The results showed that the proposed model could achieve a high accuracy of more than 90 % and a good sensitivity of more than 96 % for each disease, indicating the overwhelming advantage of the mIEF method combined with deep learning in contrast to the pure mIEF method.
CONCLUSIONS: Overall, the presented method of mIEF with deep learning enabled, for the first time, the absolute quantitative detection of Hb, relative quantitation of Hb species, and simultaneous screening of diabetes, anemia, alpha-thalassemia, and beta-thalassemia. The AI-based diagnosis assistant system combined with mIEF, we believe, will help doctors and specialists perform fast and precise disease screening in the future.
摘要：
背景：血红蛋白（Hb）是红细胞中的重要蛋白质，是疾病的关键诊断指标，例如，糖尿病,地中海贫血,和贫血。然而,关于同时筛查糖尿病的方法很少见，贫血,和地中海贫血。等电聚焦（IEF）是分离和分析Hb的常用分离工具。然而,目前对IEF图像的分析耗时,不能用于同时筛查.因此,IEF图像识别的人工智能(AI)是准确的，敏感,低成本筛查。
结果：这里，提出了一种基于微带等电聚焦（mIEF）的Hb相对含量检测方法。通过常规自动血液学分析仪的Hb定量与通过mIEF的Hb定量之间存在良好的一致性,其中R2=0.9898。然而，我们的结果表明，仅基于Hb物种的定量进行疾病诊断的准确性低至69.33％，特别是同时筛查糖尿病的多种疾病，贫血,α-地中海贫血,和β-地中海贫血.因此,我们引入了一种基于ResNet1D的诊断模型,以提高多种疾病的筛查准确率.结果表明，所提出的模型对每种疾病都能达到90%以上的高精度和96%以上的良好灵敏度，与纯mIEF方法相比，mIEF方法与深度学习相结合具有压倒性优势。
结论：总体而言，所提出的支持深度学习的MIEF方法，第一次,Hb的绝对定量检测，Hb物种的相对定量，同时筛查糖尿病，贫血,α-地中海贫血,和β-地中海贫血.基于AI的诊断辅助系统结合mIEF，我们相信,将帮助医生和专家在未来进行快速和精确的疾病筛查。
结果：这里，提出了一种基于微带等电聚焦（mIEF）的Hb相对含量检测方法。通过常规自动血液学分析仪的Hb定量与通过mIEF的Hb定量之间存在良好的一致性,其中R2=0.9898。然而，我们的结果表明，仅基于Hb物种的定量进行疾病诊断的准确性低至69.33％，特别是同时筛查糖尿病的多种疾病，贫血,α-地中海贫血,和β-地中海贫血.因此,我们引入了一种基于ResNet1D的诊断模型,以提高多种疾病的筛查准确率.结果表明，所提出的模型对每种疾病都能达到90%以上的高精度和96%以上的良好灵敏度，与纯mIEF方法相比，mIEF方法与深度学习相结合具有压倒性优势。
结论：总体而言，所提出的支持深度学习的MIEF方法，第一次,Hb的绝对定量检测，Hb物种的相对定量，同时筛查糖尿病，贫血,α-地中海贫血,和β-地中海贫血.基于AI的诊断辅助系统结合mIEF，我们相信,将帮助医生和专家在未来进行快速和精确的疾病筛查。
