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

Autor: Chung-Yueh Lien, Tseng-Tse Chen, En-Tung Tsai, Yu-Jer Hsiao, Ni Lee, Chong-En Gao, Yi-Ping Yang, Shih-Jen Chen, Aliaksandr A. Yarmishyn, De-Kuang Hwang, Shih-Jie Chou, Woei-Chyn Chu, Shih-Hwa Chiou, Yueh Chien
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Cells, Vol 12, Iss 2, p 211 (2023)
Druh dokumentu: article
ISSN: 2073-4409
DOI: 10.3390/cells12020211
Popis: 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.
Databáze: Directory of Open Access Journals
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