Adherent cell lines are widely used across all fields of biology, including drug discovery, toxicity studies, and regenerative medicine. However, adherent cell processes are often limited by a lack of advances in cell culture systems. While suspension culture processes benefit from decades of development of instrumented bioreactors, adherent cultures are typically performed in static, noninstrumented flasks and well-plates. We previously described a microfabricated bioreactor that enables a high degree of control on the microenvironment of the cells while remaining compatible with standard cell culture protocols. In this report, we describe its integration with automated image-processing capabilities, allowing the continuous monitoring of key cell culture characteristics. A machine learning-based algorithm enabled the specific detection of one cell type within a co-culture setting, such as human embryonic stem cells against the background of fibroblast cells. In addition, the algorithm did not confuse image artifacts resulting from microfabrication, such as scratches on surfaces, or dust particles, with cellular features. We demonstrate how the automation of flow control, environmental control, and image acquisition can be employed to image the whole culture area and obtain time-course data of mouse embryonic stem cell cultures, for example, for confluency
