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Recognizing the Differentiation Degree of Human Induced Pluripotent Stem Cell-Derived Retinal Pigment Epithelium Cells Using Machine Learning and Deep Learning-Based Approaches. | LitMetric

AI Article Synopsis

  • Induced pluripotent stem cells (iPSCs) can transform into various specialized cells needed for regenerative medicine, but their production is labor-intensive and slow.
  • This study introduces a new AI-assisted model, the multi-slice tensor model, which uses a modified CNN to classify iPSC-derived cells and assess their differentiation levels effectively.
  • The model achieved a high classification accuracy of 97.8% for different cell types and could differentiate between mature and immature cells, supporting the potential of using iPSCs in clinical applications like cell transplantation therapy.

Article Abstract

Induced pluripotent stem cells (iPSCs) can be differentiated into mesenchymal stem cells (iPSC-MSCs), retinal ganglion cells (iPSC-RGCs), and retinal pigmental epithelium cells (iPSC-RPEs) to meet the demand of regeneration medicine. Since the production of iPSCs and iPSC-derived cell lineages generally requires massive and time-consuming laboratory work, artificial intelligence (AI)-assisted approach that can facilitate the cell classification and recognize the cell differentiation degree is of critical demand. In this study, we propose the multi-slice tensor model, a modified convolutional neural network (CNN) designed to classify iPSC-derived cells and evaluate the differentiation efficiency of iPSC-RPEs. We removed the fully connected layers and projected the features using principle component analysis (PCA), and subsequently classified iPSC-RPEs according to various differentiation degree. With the assistance of the support vector machine (SVM), this model further showed capabilities to classify iPSCs, iPSC-MSCs, iPSC-RPEs, and iPSC-RGCs with an accuracy of 97.8%. In addition, the proposed model accurately recognized the differentiation of iPSC-RPEs and showed the potential to identify the candidate cells with ideal features and simultaneously exclude cells with immature/abnormal phenotypes. This rapid screening/classification system may facilitate the translation of iPSC-based technologies into clinical uses, such as cell transplantation therapy.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9856279PMC
http://dx.doi.org/10.3390/cells12020211DOI Listing

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