| Sumario: | Stem cells are critical tools in regenerative medicine, large-scale cell production, drug discovery, and cell-based therapies, making their precise identification and sorting essential for advancing both research and clinical applications. Accurate stem cell sorting enables improved therapeutic outcomes, efficient production pipelines, and more reliable biological studies. Traditional sorting methods, while effective, face challenges related to speed, scalability, cost, and human error. Recent advances in machine learning (ML) techniques based on image and video processing have revolutionized stem cell sorting by enabling rapid, automated, and highly accurate classification. In addition to visual data approaches, non-visual processing methods using ML have also emerged as powerful tools for stem cell analysis and separation. In this review, various ML-driven strategies for stem cell sorting, with a particular focus on visual and non-visual data processing methodologies and their applications in different stem cell types, have been comprehensively explored and categorized based on the input data types, ML techniques, stem cell types, study objectives, and performance metrics. Furthermore, an overview of the historical development of stem cell sorting technologies and ML applications was introduced, and emerging automated systems, software solutions, start-ups, and future directions for this type of stem cell sorters were discussed.
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