Stem cell imaging through convolutional neural networks: current issues and future directions in artificial intelligence technology

PeerJ

article

Ramanaesh Rao Ramakrishna¹  Zariyantey Abd Hamid¹  Wan Mimi Diyana Wan Zaki²  Aqilah Baseri Huddin²  Ramya Mathialagan¹ 

[1] Biomedical Science Programme and Centre for Diagnostic, Therapeutic and Investigative Science, Faculty of Health Sciences, Universiti Kebangsaan Malaysia;Department of Electrical, Electronic & Systems Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia

关键词: Stem cell;    Biomedical imaging;    Artificial intelligence;    Machine learning;    Deep learning;    Convolutional neural network;    Induced pluripotent stem cell;    Hematopoietic stem cell;    Medical analysis;    Morphology and pattern recognition;   

DOI  :  10.7717/peerj.10346

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY