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人工智能辅助流式细胞术分析在免疫紊乱中的验证

Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders.

作者信息

Lu Zhengchun, Morita Mayu, Yeager Tyler S, Lyu Yunpeng, Wang Sophia Y, Wang Zhigang, Fan Guang

机构信息

Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA.

DeepCyto LLC, West Linn, OR 97068, USA.

出版信息

Diagnostics (Basel). 2024 Feb 14;14(4):420. doi: 10.3390/diagnostics14040420.

DOI:10.3390/diagnostics14040420
PMID:38396459
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10888253/
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 ( > 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

流式细胞术是血液学和免疫性疾病的重要诊断工具，但手动分析容易出现差异且耗时。在过去十年中，人工智能（AI）取得了显著进展。在本研究中，我们使用2021年的379例临床病例开发并验证了一种人工智能辅助的流式细胞术工作流程，采用了一个3管、10色的流式检测板，配备21种抗体，用于原发性免疫缺陷疾病和相关免疫性疾病。人工智能软件（DeepFlow™，版本2.1.1）是完全自动化的，将每个病例的分析时间缩短至5分钟以内。必要时，它会与血液病理学家互动进行手动设门调整。使用专有的多维密度-表型耦合算法，人工智能模型能够准确地对T细胞、B细胞和NK细胞以及重要的免疫细胞亚群进行分类和计数，包括CD4+辅助性T细胞、CD8+细胞毒性T细胞、CD3+/CD4-/CD8-双阴性T细胞以及类别转换或未转换的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，人工智能模型在淋巴细胞亚群中表现出很强的相关性（>0.9）。本研究强调了人工智能辅助流式细胞术在临床环境中诊断免疫性疾病的准确性和效率，在短时间内提供了一种变革性方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ad5/10888253/717130960c43/diagnostics-14-00420-g007.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ad5/10888253/717130960c43/diagnostics-14-00420-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ad5/10888253/74fd15752593/diagnostics-14-00420-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ad5/10888253/4f10cb8d2433/diagnostics-14-00420-g002.jpg
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