StemGuard : An Expertise‐Reinforced and Voice‐Controlled Agent for Stem Cell Culture Surveillance

Vitro stem cell culture is vital for developing antiaging and antiallergy drugs. Cell confluence acts as a core indicator of cell proliferation and culture status. However, confluence estimation mostly depends on manual labour, as there are few artificial‐intelligence (AI) models or professional software developed for the task. Although most existing AI works adopted cell segmentation networks for confluence estimation, their efficacy and efficiency need improving. Meanwhile, the growth status of the cells is daily monitored with their images captured and archived by technicians using microscopes. As a result, cell images are constantly generated, exhibiting large data variance and posing challenges to the estimation models. Therefore, we propose StemGuard, perhaps the novel whole‐process AI agent specified at cell confluence estimation. It completes the basic confluence estimation task by a novel backbone that leverages categorization and feature fusion. It features an expertise‐reinforced optimization mechanism, which enables adaptive learning on the dynamic data. It also includes a voice control module for better use experience in sterile laboratories. In addition, we define a novel index, Average Confusion Degree (ACD), to assess confluence estimation. It measures the confluence error range rather than the raw error, as the former is more meaningful than the latter to the technicians. Evaluated on real microscopic cell images provided by a drug company, StemGuard runs smoothly and delivers state‐of‐the‐art cell confluence estimation performance, achieving an ACD of 0.0421 and Mean Absolute Error (MAE) of 0.0638.