ArticleScientific reports2022
Multimodal NASH prognosis using 3D imaging flow cytometry and artificial intelligence to characterize liver cells.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Three-dimensional forward-scattering imaging flow cytometry system for single-cell analysis.APL photonics · 2025Article
- Highly Efficient Calibration-Free Color Compensation Algorithm for Imaging Flow Cytometry.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2025Article
- Review
- Imaging Flow Cytometry: Development, Present Applications, and Future Challenges.Methods and protocols · 2024Review
- Interpretable unsupervised learning enables accurate clustering with high-throughput imaging flow cytometry.Scientific reports · 2023Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To improve the understanding of the complex biological process underlying the development of non-alcoholic steatohepatitis (NASH), 3D imaging flow cytometry (3D-IFC) with transmission and side-scattered images were used to characterize hepatic stellate cell (HSC) and liver endothelial cell (LEC) morphology at single-cell resolution. In this study, HSC and LEC were obtained from biopsy-proven NASH subjects with early-stage NASH (F2-F3) and healthy controls. Here, we applied single-cell imaging and 3D digital reconstructions of healthy and diseased cells to analyze a spatially resolved set of morphometric cellular and texture parameters that showed regression with disease progression. By developing a customized autoencoder convolutional neural network (CNN) based on label-free cell transmission and side scattering images obtained from a 3D imaging flow cytometer, we demonstrated key regulated cell types involved in the development of NASH and cell classification performance superior to conventional machine learning methods.
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Registered trials
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