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DeepACT in QC for Cultured Stem Cells

In their new STEM CELLS article, researchers led by Jun'ichi Kotoku (Teikyo University) and Daisuke Nanba (Tokyo Medical and Dental University, Tokyo, Japan) report on the development of a novel non-invasive quality control technology for cultured human keratinocyte stem cells constructed by deep learning‐based automated cell recognition and Kalman filter algorithm‐based tracking. Hirose et al. describe how this deep learning‐based automated cell tracking (DeepACT) technology rapidly analyzed keratinocytes' motion and provided collective motion dynamics of cultured keratinocytes, which enabled the quantitative evaluation of keratinocyte dynamics in response to changes in culture conditions. Furthermore, DeepACT identified human keratinocyte stem cells, as the stem cell colonies exhibited a unique motion pattern.