ArticleChem & bio engineering2025
Supervised-Learning-Driven Interrogation of Organ-on-a-Chip Quality from Microscopy Images.
Article in Chem & bio engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The trial behind it
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Who cites it
4 citing papers in PubMed.
- Liver-on-a-Chip (LoC) Models: Case Studies of Academic Platforms and Commercial Products.Molecular pharmaceutics · 2026Review
- Unveiling Particulate Matter-Lung Interactions from Static Models to Dynamic Lung-on-Chips.Research (Washington, D.C.) · 2026Review
- Female Reproductive Tract Modeling through Advanced 3D Biomimetic Platforms.Biomaterials research · 2026Review
- Organ-on-a-chip platforms for disease modeling and in vitro diagnostic applications.Frontiers in bioengineering and biotechnology · 2026Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We leverage supervised learning to realize high-throughput and objective interrogation of organ-on-a-chip quality from microscopy images. More than 600 images of two lung-related cell types in good and bad quality lung-on-a-chip models are assessed. We demonstrate that trained classifiers can predict different cell types and lung-on-a-chip quality with AUC and accuracy of above 95% and 83%, respectively. Coupled with a dimensionality reduction approach, the predictive capacity of certain classifiers can be enhanced, while the computational cost of resource-intensive algorithms can be significantly reduced. This study is anticipated to further encourage the implementation of machine learning for automating organ-on-a-chip development.
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Registered trials
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