  Flow-Imaging Microscopy (FIM) is commonly used in both academia and industry
to characterize subvisible particles (those $\le 25 \mu m$ in size) in protein
therapeutics. Pharmaceutical companies are required to record vast volumes of
FIM data on protein therapeutic products, but are only mandated under US FDA
regulations (i.e., USP $\big \langle 788 \big \rangle$) to control the number
of particles exceeding $10$ and $25 \mu m$ in delivered products. Hence, a vast
amount of digital images are available to analyze. Current state-of-the-art
methods rely on a relatively low-dimensional list of "morphological features"
to characterize particles, but these methods ignore an enormous amount of
information encoded in the existing large digital image repositories. Deep
Convolutional Neural Networks (CNNs or "ConvNets") have demonstrated the
ability to extract predictive information from raw macroscopic image data
without requiring the selection or specification of "morphological features" in
a variety of tasks. However, the heterogeneity, polydispersity of protein
therapeutics, and optical phenomena associated with subvisible FIM particle
measurements introduce new challenges regarding the application of CNNs to FIM
image analysis. In this article, we demonstrate a supervised learning technique
leveraging CNNs to extract information from raw images in order to predict the
process conditions or stress states (freeze-thaw, mechanical shaking, etc.)
that produced a variety of different protein images. We demonstrate that our
new classifier (in combination with a sample "image pooling" strategy) can
obtain nearly perfect predictions using as few as 20 FIM images from a given
protein formulation in a variety of scenarios of relevance to protein
therapeutics quality control and process monitoring.
