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Stem cell imaging through convolutional neural networks: current issues and future directions in artificial intelligence technology
PeerJ ( IF 2.7 ) Pub Date : 2020-11-18 , DOI: 10.7717/peerj.10346
Ramanaesh Rao Ramakrishna 1 , Zariyantey Abd Hamid 1 , Wan Mimi Diyana Wan Zaki 2 , Aqilah Baseri Huddin 2 , Ramya Mathialagan 1
Affiliation  

Stem cells are primitive and precursor cells with the potential to reproduce into diverse mature and functional cell types in the body throughout the developmental stages of life. Their remarkable potential has led to numerous medical discoveries and breakthroughs in science. As a result, stem cell–based therapy has emerged as a new subspecialty in medicine. One promising stem cell being investigated is the induced pluripotent stem cell (iPSC), which is obtained by genetically reprogramming mature cells to convert them into embryonic-like stem cells. These iPSCs are used to study the onset of disease, drug development, and medical therapies. However, functional studies on iPSCs involve the analysis of iPSC-derived colonies through manual identification, which is time-consuming, error-prone, and training-dependent. Thus, an automated instrument for the analysis of iPSC colonies is needed. Recently, artificial intelligence (AI) has emerged as a novel technology to tackle this challenge. In particular, deep learning, a subfield of AI, offers an automated platform for analyzing iPSC colonies and other colony-forming stem cells. Deep learning rectifies data features using a convolutional neural network (CNN), a type of multi-layered neural network that can play an innovative role in image recognition. CNNs are able to distinguish cells with high accuracy based on morphologic and textural changes. Therefore, CNNs have the potential to create a future field of deep learning tasks aimed at solving various challenges in stem cell studies. This review discusses the progress and future of CNNs in stem cell imaging for therapy and research.

中文翻译:

通过卷积神经网络进行干细胞成像:人工智能技术的当前问题和未来方向

干细胞是原始细胞和前体细胞,在生命的各个发育阶段都有可能在体内繁殖成多种成熟和功能性细胞类型。它们的非凡潜力带来了众多医学发现和科学突破。因此,基于干细胞的治疗已成为医学上的一个新的亚专业。正在研究的一种有前途的干细胞是诱导多能干细胞(iPSC),它是通过对成熟细胞进行基因重编程以将其转化为胚胎样干细胞而获得的。这些 iPSC 用于研究疾病的发生、药物开发和医学疗法。然而,iPSC 的功能研究涉及通过手动识别来分析 iPSC 衍生的集落,这非常耗时、容易出错且依赖于训练。因此,需要一种用于分析 iPSC 集落的自动化仪器。最近,人工智能(AI)作为应对这一挑战的新技术应运而生。特别是,深度学习(人工智能的一个子领域)提供了一个用于分析 iPSC 集落和其他集落形成干细胞的自动化平台。深度学习使用卷积神经网络(CNN)来纠正数据特征,卷积神经网络是一种多层神经网络,可以在图像识别中发挥创新作用。CNN 能够根据形态和结构的变化高精度地区分细胞。因此,CNN 有潜力创建未来的深度学习任务领域,旨在解决干细胞研究中的各种挑战。这篇综述讨论了 CNN 在干细胞成像治疗和研究方面的进展和未来。
更新日期:2020-11-18
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