PDF(Pubmed)
Abstract：
Flow cytometry is a vital diagnostic tool for hematologic and immunologic disorders, but manual analysis is prone to variation and time-consuming. Over the last decade, artificial intelligence (AI) has advanced significantly. In this study, we developed and validated an AI-assisted flow cytometry workflow using 379 clinical cases from 2021, employing a 3-tube, 10-color flow panel with 21 antibodies for primary immunodeficiency diseases and related immunological disorders. The AI software (DeepFlow™, version 2.1.1) is fully automated, reducing analysis time to under 5 min per case. It interacts with hematopatholoists for manual gating adjustments when necessary. Using proprietary multidimensional density-phenotype coupling algorithm, the AI model accurately classifies and enumerates T, B, and NK cells, along with important immune cell subsets, including CD4+ helper T cells, CD8+ cytotoxic T cells, CD3+/CD4-/CD8- double-negative T cells, and class-switched or non-switched B cells. Compared to manual analysis with hematopathologist-determined lymphocyte subset percentages as the gold standard, the AI model exhibited a strong correlation (r > 0.9) across lymphocyte subsets. This study highlights the accuracy and efficiency of AI-assisted flow cytometry in diagnosing immunological disorders in a clinical setting, providing a transformative approach within a concise timeframe.
摘要：
流式细胞术是血液和免疫疾病的重要诊断工具。但是人工分析容易变化且耗时。在过去的十年里，人工智能(AI)取得了显著进步。在这项研究中,我们从2021年开始使用379例临床病例开发并验证了AI辅助流式细胞术工作流程，采用3管，原发性免疫缺陷疾病和相关免疫疾病的21种抗体的10色流动图。AI软件(DeepFlow™,版本2.1.1)是完全自动化的，将分析时间减少到每例5分钟以下。必要时，它与血液病理学家互动以进行手动门控调整。使用专有的多维密度-表型耦合算法，AI模型准确地对T进行分类和枚举，B,和NK细胞，以及重要的免疫细胞亚群，包括CD4+辅助性T细胞，CD8+细胞毒性T细胞，CD3+/CD4-/CD8-双阴性T细胞，和类交换或非交换B细胞。与以血液病理学家确定的淋巴细胞亚群百分比作为金标准的手动分析相比，AI模型在淋巴细胞亚群之间表现出强相关性(r>0.9)。这项研究强调了AI辅助流式细胞术在临床环境中诊断免疫疾病的准确性和效率。在简洁的时间框架内提供变革性的方法。
