PDF(Pubmed)
Abstract：
Myelodysplastic syndrome is primarily characterized by dysplasia in the bone marrow (BM), presenting a challenge in consistent morphology interpretation. Accurate diagnosis through traditional slide-based analysis is difficult, necessitating a standardized objective technique. Over the past two decades, imaging flow cytometry (IFC) has proven effective in combining image-based morphometric analyses with high-parameter phenotyping. We have previously demonstrated the effectiveness of combining IFC with a feature-based machine learning algorithm to accurately identify and quantify rare binucleated erythroblasts (BNEs) in dyserythropoietic BM cells. However, a feature-based workflow poses challenges requiring software-specific expertise. Here we employ a Convolutional Neural Network (CNN) algorithm for BNE identification and differentiation from doublets and cells with irregular nuclear morphology in IFC data. We demonstrate that this simplified AI workflow, coupled with a powerful CNN algorithm, achieves comparable BNE quantification accuracy to manual and feature-based analysis with substantial time savings, eliminating workflow complexity. This streamlined approach holds significant clinical value, enhancing IFC accessibility for routine diagnostic purposes.
摘要：
骨髓增生异常综合征的主要特征是骨髓（BM）的发育不良，在一致的形态学解释中提出了挑战。通过传统的基于幻灯片的分析进行准确的诊断是困难的,需要标准化的客观技术。在过去的二十年里，成像流式细胞术（IFC）已被证明可以有效地将基于图像的形态测量分析与高参数表型分析相结合。我们先前已经证明了将IFC与基于特征的机器学习算法相结合以准确识别和量化红细胞生成异常BM细胞中的稀有双核成红细胞（BNE）的有效性。然而,基于功能的工作流程带来了需要软件特定专业知识的挑战。在这里，我们采用卷积神经网络（CNN）算法，用于从IFC数据中具有不规则核形态的双峰和细胞中进行BNE识别和分化。我们证明了这个简化的人工智能工作流程，加上强大的CNN算法，实现了与手动和基于特征的分析相当的BNE量化精度，节省了大量时间，消除工作流的复杂性。这种简化的方法具有重要的临床价值，增强用于常规诊断目的的IFC可访问性。
