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AI-driven Quality Control of cell-based ATMPs: Automated, Accurate, and Affordable

Magnusson, Rasmus ; Hägg, Alice LU orcid ; Twohig, Daniel LU orcid ; Savchenko, Ekaterina LU ; Ghosheh, Nidal ; Melguizo-Sanchis, Dario ; Falk, Anna LU orcid and Synnergren, Jane (2026) In Cytotherapy
Abstract
The production of neuroepithelial stem (NES) cells, a promising therapeutic candidate for neurological conditions such as stroke and spinal cord injuries, faces significant manufacturing challenges, particularly in quality control (QC). Specifically, current QC methods rely on labor-intensive manual assessments that are costly, time-consuming, and subject to operator bias, limiting scalability and reproducibility. This study addresses these limitations by developing a Convolutional Neural Network (CNN)-based model for automating morphological QC during NES cell expansion. The model achieved an area under the receiver operating characteristic curve (AUROC) of > 0.997 when applied to test data, with robustness validated via image rotation... (More)
The production of neuroepithelial stem (NES) cells, a promising therapeutic candidate for neurological conditions such as stroke and spinal cord injuries, faces significant manufacturing challenges, particularly in quality control (QC). Specifically, current QC methods rely on labor-intensive manual assessments that are costly, time-consuming, and subject to operator bias, limiting scalability and reproducibility. This study addresses these limitations by developing a Convolutional Neural Network (CNN)-based model for automating morphological QC during NES cell expansion. The model achieved an area under the receiver operating characteristic curve (AUROC) of > 0.997 when applied to test data, with robustness validated via image rotation and segmentation. The model successfully distinguished key morphological features, such as rosette-like structures in high-quality cultures, and detected pronounced heterogeneity in low-quality samples. Complementary flow cytometry validation confirmed the biological relevance of the model’s predictions. By integrating this CNN-based QC system, we offer a scalable and cost-effective solution for real-time monitoring, batch consistency, and reduced process variability. This approach supports the clinical translation of NES cell therapies and holds broader applicability across other cell-based advanced therapy medicinal products. (Less)
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author
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organization
publishing date
type
Contribution to journal
publication status
epub
subject
in
Cytotherapy
article number
102135
publisher
Taylor & Francis
ISSN
1477-2566
DOI
10.1016/j.jcyt.2026.102135
language
English
LU publication?
yes
id
92c77c86-b8a2-4a07-92aa-5f2c23292e94
date added to LUP
2026-03-19 13:22:30
date last changed
2026-03-19 13:39:04
@article{92c77c86-b8a2-4a07-92aa-5f2c23292e94,
  abstract     = {{The production of neuroepithelial stem (NES) cells, a promising therapeutic candidate for neurological conditions such as stroke and spinal cord injuries, faces significant manufacturing challenges, particularly in quality control (QC). Specifically, current QC methods rely on labor-intensive manual assessments that are costly, time-consuming, and subject to operator bias, limiting scalability and reproducibility. This study addresses these limitations by developing a Convolutional Neural Network (CNN)-based model for automating morphological QC during NES cell expansion. The model achieved an area under the receiver operating characteristic curve (AUROC) of > 0.997 when applied to test data, with robustness validated via image rotation and segmentation. The model successfully distinguished key morphological features, such as rosette-like structures in high-quality cultures, and detected pronounced heterogeneity in low-quality samples. Complementary flow cytometry validation confirmed the biological relevance of the model’s predictions. By integrating this CNN-based QC system, we offer a scalable and cost-effective solution for real-time monitoring, batch consistency, and reduced process variability. This approach supports the clinical translation of NES cell therapies and holds broader applicability across other cell-based advanced therapy medicinal products.}},
  author       = {{Magnusson, Rasmus and Hägg, Alice and Twohig, Daniel and Savchenko, Ekaterina and Ghosheh, Nidal and Melguizo-Sanchis, Dario and Falk, Anna and Synnergren, Jane}},
  issn         = {{1477-2566}},
  language     = {{eng}},
  month        = {{02}},
  publisher    = {{Taylor & Francis}},
  series       = {{Cytotherapy}},
  title        = {{AI-driven Quality Control of cell-based ATMPs: Automated, Accurate, and Affordable}},
  url          = {{http://dx.doi.org/10.1016/j.jcyt.2026.102135}},
  doi          = {{10.1016/j.jcyt.2026.102135}},
  year         = {{2026}},
}