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Material Measurement Laboratory

Biosystems and Biomaterials Division

FCSC Working Group 5

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Schematic of a three stage process. Stage 1 flow cytometry data and clinical data and/or information are collected as inputs. Stage 2 inputs are used to build an AI/ML model that can be predictive, clustering, generative or large language. Stage 3 applies the model to predict clinical outcomes, identify and/or correlate biomarkers, and cluster by cell type or function. Rounds of generating hypotheses and updating data or models iteratively feed back into stages 1 and 2 to generate new results.

Applying AI/ML approaches to flow cytometry measurements and clinical data to predict patient outcomes, relate biomarkers and cluster cells by type and/or function.

Artificial Intelligence and Machine Learning (AI/ML) Approaches

Working Group 5 (WG5), once launched, will use high-quality reference datasets generated by Consortium interlaboratory studies to support the development and validation of emerging AI/ML models for flow cytometry.  Activities in progress or planned include:

  1. NIST–FDA–NIAID Joint Workshop on AI and Flow Cytometry, March 26 and 27, 2025 The workshop will focus on overcoming challenges and identifying solutions by optimizing existing standards, including essential measurements, reference controls, AI-ready reference datasets, and AI/ML models.  These efforts will target emerging applications in disease diagnosis, therapeutic development, manufacturing, clinical trials, and in-clinic dosing and monitoring.
Bioscience, Biomanufacturing, Cell biology, Gene delivery systems, Health, Cell and gene therapy, Precision medicine, Regenerative medicine and advanced therapy, Metrology, Amount of substance, Standards and Reference materials

Contacts

Consortium Principal Investigator

Created November 29, 2024