Stem cell-based products have clinical and industrial applications. Thus, there is a need to develop quality control methods to standardize stem cell manufacturing. Here, we report a deep learning-based automated cell tracking (DeepACT) technology for noninvasive quality control and identification of cultured human stem cells. The combination of deep learning-based cascading cell detection and Kalman filter algorithm-based tracking successfully tracked the individual cells within the densely packed human epidermal keratinocyte colonies in the phase-contrast images of the culture. DeepACT rapidly analyzed the motion of individual keratinocytes, which enabled the quantitative evaluation of keratinocyte dynamics in response to changes in culture conditions. Furthermore, DeepACT can distinguish keratinocyte stem cell colonies from non-stem cell-derived colonies by analyzing the spatial and velocity information of cells. This system can be widely applied to stem cell cultures used in regenerative medicine and provides a platform for developing reliable and noninvasive quality control technology. K E Y W O R D S cell motion analysis, deep learning, keratinocyte stem cells, quality control, stem cell cultures 1 | INTRODUCTION Stem cells, including pluripotent and tissue-specific stem cells have various clinical applications. 1 The quality of the cultured stem cells is crucial for successful application in regenerative medicine. Hence, these stem cells must be maintained and expanded ex vivo under carefully controlled conditions. However, the quality control of stem cell cultures is currently based on visual inspection by individual cell culture experts, which hinders the standardization of stem cell cultures and large-scale stem cell manufacturing for applications in regenerative medicine. Takuya Hirose and Jun'ichi Kotoku contributed equally to this study.