| Autor: |
Rodrigues MA; Amnis Flow Cytometry, Luminex Corporation; mrodrigues@luminexcorp.com., Gracia García Mendoza M; Amnis Flow Cytometry, Luminex Corporation., Kong R; Amnis Flow Cytometry, Luminex Corporation., Sutton A; Amnis Flow Cytometry, Luminex Corporation., Pugsley HR; Amnis Flow Cytometry, Luminex Corporation., Li Y; Amnis Flow Cytometry, Luminex Corporation., Hall BE; Amnis Flow Cytometry, Luminex Corporation., Fogg D; Amnis Flow Cytometry, Luminex Corporation., Ohl L; Amnis Flow Cytometry, Luminex Corporation., Venkatachalam V; Amnis Flow Cytometry, Luminex Corporation. |
| Abstrakt: |
The micronucleus (MN) assay is used worldwide by regulatory bodies to evaluate chemicals for genetic toxicity. The assay can be performed in two ways: by scoring MN in once-divided, cytokinesis-blocked binucleated cells or fully divided mononucleated cells. Historically, light microscopy has been the gold standard method to score the assay, but it is laborious and subjective. Flow cytometry has been used in recent years to score the assay, but is limited by the inability to visually confirm key aspects of cellular imagery. Imaging flow cytometry (IFC) combines high-throughput image capture and automated image analysis, and has been successfully applied to rapidly acquire imagery of and score all key events in the MN assay. Recently, it has been demonstrated that artificial intelligence (AI) methods based on convolutional neural networks can be used to score MN assay data acquired by IFC. This paper describes all steps to use AI software to create a deep learning model to score all key events and to apply this model to automatically score additional data. Results from the AI deep learning model compare well to manual microscopy, therefore enabling fully automated scoring of the MN assay by combining IFC and AI. |
