ArticlePharmaceutical research2023
Convolutional Neural Networks Enable Highly Accurate and Automated Subvisible Particulate Classification of Biopharmaceuticals.
Article in Pharmaceutical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Optical imaging and spectroscopic characterization of subvisible particles in protein therapeutics.Advanced drug delivery reviews · 2026Review
- Emerging Technologies and Integrated Interdisciplinary Strategies for Mitigating Protein Aggregation in Therapeutic Formulations.Pharmaceutical research · 2026Review
- Real-Time Assessment of the Size Changes of Individual Sub-Visible Protein Particles under Buffer Variations: A Microfluidic Study.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Enabling Efficient Design of Biological Formulations through Advanced Characterizations.Pharmaceutical research · 2023Article
- Artificial intelligence and machine learning-assisted digital applications for biopharmaceutical manufacturing.Biotechnology progressReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantification of subvisible particles, which are generally defined as those ranging in size from 2 to 100 µm, is important as critical characteristics for biopharmaceutical formulation development. Micro Flow Imaging (MFI) provides quantifiable morphological parameters to study both the size and type of subvisible particles, including proteinaceous particles as well as non-proteinaceous features incl. silicone oil droplets, air bubble droplets, etc., thus enabling quantitative and categorical particle attribute reporting for quality control. However, limitations in routine MFI image analysis can hinder accurate subvisible particle classification. In this work, we custom-built a subvisible particle-aware Convolutional Neural Network, SVNet, which has a very small computational footprint, and achieves comparable performance to prior state-of-art image classification models. SVNet significantly improves upon current standard operating procedures for subvisible particulate assessments as confirmed by thorough real-world validation studies.
Indexed as
Identifiers
36471026What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.