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