Small and Medium Scale Automation in iPS cell Culture utilizing AI Based Learning and Machine Vision

In this paper, we propose a concept for AI and machine vision for the observation and assessment of induced pluripotent stem (iPS) cell culture based on a differentiation of the two major monitoring aspects of cell expansion-cell health and cell density. The proposed concept is part of a more holistic approach to automating cell cultures in smaller laboratories. The concept embedded the broader automation of basic cell culture procedures using iPS cells and expansion protocols, as well as implementing mechanisms and a multipurpose gripper for discarding old and refilling fresh culture media and handling individual vessels.Based on phase contrast microscopy imaging our approach involves utilizing machine vision algorithms to calculate cell density and decide whether to split the culture or not. We implemented an image algorithm to achieve this and used threshold values derived from the working experiences with iPS cells. We concluded that our approach's achieved accuracy is sufficient to automate this task of cell assessment. The process is modular so that the proposed algorithms can be implemented with manually taken images or fully automated imaging.A trained image-based AI can be used to obtain a decision if the shown phase contrast image only shows healthy cells. If there are any not trained formations, the cells will be discarded. Future work includes completing the set of images for training the AI to recognize any deviation i.e. contamination, premature differentiation, or cell death for the health aspect of cell expansion monitoring. We aim to streamline and automate basic iPS cell culture procedures and make them more accessible to smaller laboratories. The proposed algorithms for cell health and available growing space assessment seem promising and may help with decision-making and ensure the health and growth of the iPS cells.