57: Improved Deep Learning Classification of Sub-visible Particles in Biopharmaceuticals

Music City Center 

Sub-visible particles (ranging from 2-100 µm in size) within liquid injectable sterile biopharmaceutical formulations require quantification and analysis as a critical component of formulation development. Micro-flow imaging (MFI) enables high-resolution visualization of sub-visible particles, allowing scientists to develop sophisticated classification models to distinguish between high-risk proteinaceous particles and low-risk non-proteinaceous particles, such as silicone oil droplets. Previous work has demonstrated that deep learning models for image classification can enable rapid, automated classification of sub-visible particles in MFI. In this study, we enhance existing models developed for this purpose by enriching the annotated dataset, fine-tuning a pre-trained model architecture, and adjusting the training regime to improve model generalizability. We demonstrate that our new model, SVRNet, shows improved classification performance over existing methods and provides superior assessment of sub-visible particles.

biopharmaceutical

computer vision

deep learning 

Section on Statistics in Imaging