ArticlePharmaceutical research2017
A Random Forest Approach for Counting Silicone Oil Droplets and Protein Particles in Antibody Formulations Using Flow Microscopy.
Article in Pharmaceutical research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Testing Precision Limits of Neural Network-Based Quality Control Metrics in High-Throughput Digital Microscopy.Pharmaceutical research · 2022Article
- Oil-Immersion Flow Imaging Microscopy for Quantification and Morphological Characterization of Submicron Particles in Biopharmaceuticals.The AAPS journal · 2021Article
- A better method for the dynamic, precise estimating of blood/haemoglobin loss based on deep learning of artificial intelligence.Annals of translational medicine · 2020Article
- Flow Microscopy Imaging Is Sensitive to Characteristics of Subvisible Particles in Peginesatide Formulations Associated With Severe Adverse Reactions.Journal of pharmaceutical sciences · 2018Article
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Authors and funding
3 authors.
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Abstract
purposeTo evaluate a random forest model that counts silicone oil droplets and non-silicone oil particles in protein formulations with large class imbalance.
methodsIn this work, we present a novel approach for automated image analysis of flow microscopy data based on random forest classification enabling rapid analysis of large data sets. The random forest approach overcomes many of the limitations of traditional classification schemes derived from simple filters or regression models. In particular, the approach does not require a priori selection of important morphology parameters.
resultsWe analyzed silicone oil droplets and non-silicone oil particles observed in four model systems with protein concentrations of 20, 50 and 125 mg/mL. Filters based on random forests achieve higher classification accuracies when compared to regression based filters. Additionally, we showcase a procedure that allows for accurate counting of particles ≥1 μm.
conclusionsOur method is generally applicable for classification and counting of different classes of particles as long as class morphologies are differentially expressed.
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