论文标题
人工智能启用了无试剂成像血液学分析仪
Artificial Intelligence Enabled Reagent-free Imaging Hematology Analyzer
论文作者
论文摘要
白细胞差异测试是用于筛查传染病的广泛执行的临床程序。现有的血液学分析仪需要劳动密集型的工作和一组昂贵的试剂。在这里，我们报告了一种启用人工智能的无试剂成像血液学分析仪（Airfiha）模态，可以用最小的样品制备准确地对白细胞的亚群进行分类。 Airfiha是通过训练两步残差神经网络实现的，使用从定制的定量相显微镜中获取的分离白细胞的无标签图像。我们在随机选择的测试集中验证了Airfiha的性能，并在所有献血者中对其进行了交叉验证。 Airfiha在分类准确性方面的当前方法（尤其是在B和T淋巴细胞中）的表现，同时保留了自然的细胞状态。它还显示了区分CD4和CD8细胞的有希望的潜力。由于其易于操作，低成本以及复杂的白细胞亚群的强大辨别能力，我们设想Airfiha在临床上可以翻译，并且也可以在资源有限的环境中部署，例如在大流行状况中以快速筛查感染性疾病。
Leukocyte differential test is a widely performed clinical procedure for screening infectious diseases. Existing hematology analyzers require labor-intensive work and a panel of expensive reagents. Here we report an artificial-intelligence enabled reagent-free imaging hematology analyzer (AIRFIHA) modality that can accurately classify subpopulations of leukocytes with minimal sample preparation. AIRFIHA is realized through training a two-step residual neural network using label-free images of separated leukocytes acquired from a custom-built quantitative phase microscope. We validated the performance of AIRFIHA in randomly selected test set and cross-validated it across all blood donors. AIRFIHA outperforms current methods in classification accuracy, especially in B and T lymphocytes, while preserving the natural state of cells. It also shows a promising potential in differentiating CD4 and CD8 cells. Owing to its easy operation, low cost, and strong discerning capability of complex leukocyte subpopulations, we envision AIRFIHA is clinically translatable and can also be deployed in resource-limited settings, e.g., during pandemic situations for the rapid screening of infectious diseases.
