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BACKGROUND AIMS: 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. The aim of this study is to develop an automated, accurate and affordable QC method for NES cells. METHODS: This study addresses the above mentioned challenges by developing a Convolutional Neural Network (CNN)-based model for automating morphological QC during NES cell expansion. RESULTS: 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. CONCLUSION: 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.
Magnusson et al. (Wed,) studied this question.