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- Title
Recognizing the Differentiation Degree of Human Induced Pluripotent Stem Cell-Derived Retinal Pigment Epithelium Cells Using Machine Learning and Deep Learning-Based Approaches.
- Authors
Lien, Chung-Yueh; Chen, Tseng-Tse; Tsai, En-Tung; Hsiao, Yu-Jer; Lee, Ni; Gao, Chong-En; Yang, Yi-Ping; Chen, Shih-Jen; Yarmishyn, Aliaksandr A.; Hwang, De-Kuang; Chou, Shih-Jie; Chu, Woei-Chyn; Chiou, Shih-Hwa; Chien, Yueh
- 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.
- Subjects
RHODOPSIN; CHROMATOPHORES; DEEP learning; PLURIPOTENT stem cells; INDUCED pluripotent stem cells; MACHINE learning; RETINAL ganglion cells; CONVOLUTIONAL neural networks
- Publication
Cells (2073-4409), 2023, Vol 12, Issue 2, p211
- ISSN
2073-4409
- Publication type
Article
- DOI
10.3390/cells12020211