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Machine Learning-Enabled Image Comparability Assessment for Flow Imaging Microscopy Across Platforms.

Zhenhao Zhou1, Sha Guo2, Youli Tian3

  • 1School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, China.

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|January 28, 2026
PubMed
Summary
This summary is machine-generated.

Flow Imaging Microscopy (FIM) offers a promising alternative for subvisible particle analysis in biopharmaceuticals. This study developed a strategy for consistent particle counting across different FIM instruments, aiding standardization efforts.

Keywords:
flow imaging microscopy (FIM)injectable biopharmaceuticalsmachine learningstandardization studysubvisible particles

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Area of Science:

  • Biopharmaceutical analysis
  • Analytical chemistry
  • Microscopy techniques

Background:

  • Biopharmaceutical development necessitates stringent quality control, with subvisible particles being a critical attribute.
  • Current pharmacopoeial methods (Light Obscuration, Microscopic Particle Count) have limitations for advanced analytical needs.
  • Flow Imaging Microscopy (FIM) presents a potential alternative, but standardization is ongoing.

Purpose of the Study:

  • To evaluate the consistency and transferability of particle counting across different Flow Imaging Microscopy (FIM) platforms.
  • To assess the feasibility of standardized subvisible particle analysis using FIM technology.
  • To explore image classification consistency for FIM data across manufacturers.

Main Methods:

  • Instrument standardization and consistency evaluation using polystyrene microsphere standards and intravenous immunoglobulin.
  • Parallel testing of FIM instruments from different manufacturers.
  • Particle image classification using convolutional neural networks and UMAP dimensionality reduction.

Main Results:

  • A transfer strategy for consistent particle counting results across FIM platforms was developed.
  • Particle image classification demonstrated consistency in categorization but highlighted cross-platform recognition challenges.
  • The study confirmed the potential for reliable subvisible particle analysis with FIM.

Conclusions:

  • Findings contribute to the standardization of Flow Imaging Microscopy for biopharmaceutical quality control.
  • FIM shows promise as a reliable analytical tool for subvisible particle characterization.
  • Further development is needed to fully standardize cross-platform image recognition in FIM.