Revolutionizing Stem Cell Sorting with Machine Learning: A Review of Trends, Tools, and Future Directions.
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 outc...
| Publicado en: | Iranian Journal of Medical Sciences Vol. 51; no. 5; pp. 285 - 305 |
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| Autores principales: | , , , , , , |
| Formato: | pictorial review tables/charts Journal Article |
| Publicado: |
Shiraz University of Medical Sciences
May2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194404489&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194404489 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02530716 8TQH jtl: Iranian Journal of Medical Sciences issn: 02530716 maglogo: N pubinfo: dt: May2026 vid: 51 iid: 5 pid: 65846 pub: Shiraz University of Medical Sciences place: Shiraz, <Blank> artinfo: ui: 194404489 194404489 194404489 10.30476/ijms.2025.107395.4197 194404489 ppf: 285 ppct: 20 formats: tig: atl: Revolutionizing Stem Cell Sorting with Machine Learning: A Review of Trends, Tools, and Future Directions. aug: au: Mousazadeh, Marziyeh Jahangiri-Manesh, Atieh Soltaninejad, Hossein Yazdi, Farzaneh Rahimian, Karim Curran, Kathleen M. Khashayar, Patricia affil: Department of Nanobiotechnology, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran sug: subj: Stem Cells Analysis Machine Learning Utilization Cell Separation Methods Cell Separation Trends Cell Separation Equipment and Supplies Data Management Stem Cells Classification Cell Biology Artificial Intelligence Flow Cytometry Technology, Medical History Machine Learning History Autoanalyzers Cytological Techniques, Automated Software Automation, Laboratory Deep Learning Research, Medical Bioinformatics Photography Videorecording Microarray Analysis Biomechanics Machine Learning Algorithms Electrophysiology Gene Expression Image Processing, Computer Assisted ab: 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. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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