TY - JOUR
T1 - Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders
AU - Lu, Zhengchun
AU - Morita, Mayu
AU - Yeager, Tyler S.
AU - Lyu, Yunpeng
AU - Wang, Sophia Y.
AU - Wang, Zhigang
AU - Fan, Guang
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/2
Y1 - 2024/2
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - clinical cases
KW - hematopathology
KW - multiparameter flow cytometry
KW - panel for autoimmune lymphoproliferative syndrome
UR - http://www.scopus.com/inward/record.url?scp=85187243904&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85187243904&partnerID=8YFLogxK
U2 - 10.3390/diagnostics14040420
DO - 10.3390/diagnostics14040420
M3 - Article
AN - SCOPUS:85187243904
SN - 2075-4418
VL - 14
JO - Diagnostics
JF - Diagnostics
IS - 4
M1 - 420
ER -