Summary Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review - PMC www.ncbi.nlm.nih.gov
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AI technology in stem cell research has surpassed human accuracy, with the United States, China, and Japan leading the way through the use of deep learning to enhance cell identification, classification, and segmentation.
Slides
Slide Presentation (9 slides)
Key Points
- Artificial intelligence (AI) is increasingly used in stem cell research for imaging analysis and classification.
- Stem cells, including induced pluripotent stem cells (iPSCs), embryonic stem cells (ESCs), and adult stem cells (ASCs), have potential in regenerative medicine.
- AI techniques such as machine learning and convolutional neural networks assist in analyzing stem cell functions and morphology.
- The use of AI in stem cell research has seen significant growth, with studies focusing on hiPSCs, ESCs, MSCs, NSCs, and MAPCs.
- AI improves accuracy in image classification tasks and enables precise segmentation within boundary areas.
- Factors to consider in AI-based stem cell research include image quality, sample size, and unpredicted events for reliable results.
- The systematic review emphasizes the diverse applications of AI in stem cell therapy and its impact on clinical practice.
Summaries
19 word summary
AI analyzes stem cells, surpassing human accuracy. US, China, Japan lead. Deep learning improves cell identification, classification, and segmentation.
103 word summary
Artificial intelligence (AI) is increasingly used in stem cell research to analyze and classify various stem cell types, including iPSCs, ESCs, MSCs, and NSCs. Deep learning technology has improved accuracy in image classification tasks, surpassing human accuracy in some cases. The United States is the highest contributor in this field, followed by China, Japan, Brazil, and others. AI-based imaging analysis has been effective in cell identification, classification, and segmentation, using various deep learning algorithms and neural networks. The potential to revolutionize medicine is emphasized, but researchers should consider factors like image quality and sample size for reliable results in AI-based stem cell research.
136 word summary
Artificial intelligence (AI) is increasingly used in stem cell research for analyzing and classifying various stem cell types, including iPSCs, ESCs, MSCs, and NSCs. Deep learning technology has improved accuracy in image classification tasks, surpassing human accuracy in some cases and enabling pixel-level image segmentation for precise classification. A review of 27 studies found the United States to be the highest contributor in this field, followed by China, Japan, Brazil, and others. AI-based imaging analysis has been effective in cell identification, classification, and segmentation, using various deep learning algorithms and neural networks. The document provides a comprehensive overview of current trends and applications of AI in stem cell therapy, emphasizing the potential to revolutionize medicine. However, researchers should consider factors like image quality and sample size for reliable and reproducible results in AI-based stem cell research.
343 word summary
Artificial intelligence (AI) has gained prominence in stem cell research, particularly in the analysis and classification of various stem cell types. This includes induced pluripotent stem cells (iPSCs), embryonic stem cells (ESCs), and other stem cells such as mesenchymal stem cells (MSCs) and neural stem cells (NSCs). AI has been utilized to improve accuracy in image classification tasks, with deep learning technology surpassing human accuracy in some cases. The use of AI has also enabled the segmentation of images at the pixel level, allowing for precise classification within the boundary area.
The review of 27 studies revealed that the United States was the highest contributing country in this field, followed by China, Japan, Brazil, Taiwan, Argentina, Finland, Germany, Italy, South Korea, and Thailand. The studies have demonstrated the effectiveness of AI-based imaging analysis in tasks such as cell identification, classification, and segmentation, using various deep learning algorithms and neural networks. AI has also been used to analyze and classify different types of stem cells beyond hiPSC and ESC, such as MSCs, NSCs, and multipotent adult progenitor cells (MAPCs).
The document provides a comprehensive overview of the current trends and applications of AI in stem cell therapy. It includes a detailed analysis of the use of AI in the identification, classification, and analysis of various types of stem cells. The review also discusses the methodologies and techniques used in these studies, such as deep learning-based systems for the automated identification and classification of human embryonic stem cells. Overall, the use of AI in stem cell research has the potential to revolutionize medicine by providing accurate and efficient analysis of stem cells for various applications.
While the amount of research in this field is increasing, it is important for investigators to consider factors such as image quality, sample size, and the elimination of unexpected events that the algorithm cannot predict. Addressing these factors will be crucial for obtaining reliable and reproducible results in AI-based stem cell research. The review aims to guide and help researchers while planning for future investigations in this rapidly evolving field.
1060 word summary
Artificial intelligence (AI) has become an increasingly important tool in the analysis and identification of stem cells. Stem cells are unspecialized human cells capable of self-renewal through mitosis, eventually forming more cells. The three categories of stem cell division types consist of induced pluripotent stem cells (iPSCs), embryonic stem cells (ECSs), and adult stem cells (ACSs). The potential for stem cell implementation grows with every experiment, bringing new possibilities for transplantology and regenerative medicine. Hematopoietic stem cell research has been the most popular due to extensive experimentation and studies over the last fifty years.
The emergence of AI has made stem cells available for selecting suitable medication, establishing a diagnosis, and formulating risks and benefits when it comes to therapy. Machine learning, deep learning, and convolutional neural networks (CNN) have assisted in the reliable detection of various functions, including iPSC colony classifications, non-invasive cell therapy characterizations of normal versus abnormal cells, and image-based cellular morphology.
The search resulted in 27 studies being included in the review, with the majority of published studies occurring in 2021. The United States was the highest contributing country in this field, followed by China, Japan, Brazil, Taiwan, Argentina, Finland, Germany, Italy, South Korea, and Thailand.
In conclusion, artificial intelligence has the potential to serve as an assisting tool in stem cell imaging. The review aimed to guide and help researchers while planning for future investigations. The use of AI in stem cell research has the potential to revolutionize medicine by providing accurate and efficient analysis of stem cells for various applications.
The use of artificial intelligence (AI) in stem cell research has seen a surge in recent years. Several studies have utilized AI to analyze and classify different types of stem cells, including human-induced pluripotent stem cells (hiPSC), embryonic stem cells (ESC), and other stem cells such as mesenchymal stem cells (MSC) and neural stem cells (NSC). The application of AI in stem cell research has focused on tasks such as cell identification, classification, and segmentation, using various deep learning algorithms and neural networks.
In the context of hiPSC, several studies have demonstrated the effectiveness of AI-based imaging analysis. These studies highlight the diverse applications of AI in hiPSC research, ranging from cell formation tracing to colony feature characterization.
In the case of ESC, several studies have focused on classifying human and animal ESC using different AI algorithms. These studies demonstrate the potential of AI in accurately classifying ESC based on various differentiation-inducing conditions and categories.
In addition to hiPSC and ESC, other stem cells such as MSC, NSC, and multipotent adult progenitor cells (MAPCs) have also been studied using AI-based imaging analysis. These studies highlight the potential of AI in analyzing and classifying different types of stem cells beyond hiPSC and ESC.
The use of AI in stem cell research has several implications for the field. It has significantly improved the accuracy of image classification tasks, with deep learning technology surpassing human accuracy in some cases. AI has also enabled the segmentation of images at the pixel level, allowing for precise classification within the boundary area. However, proper annotation is still required to achieve this level of effectiveness. AI is at the forefront of accelerating progress in biomedical research and is expected to influence each stage of stem cell studies, ultimately impacting the transfer of research results to clinical practice.
While the amount of research in this field is increasing, there are important factors that investigators should consider when using AI for stem cell analysis. These factors include the quality of images used as a training set, sample size, and the elimination of unexpected events that the algorithm cannot predict. Addressing these factors will be crucial for obtaining reliable and reproducible results in AI-based stem cell research.
The document provides a systematic scoping review of the current trends and applications of artificial intelligence (AI) in stem cell therapy, focusing on the imaging analysis of stem cells. The review includes a comprehensive analysis of relevant articles and research studies that utilize AI for the identification, classification, and analysis of stem cells.
The review covers a wide range of topics related to stem cell therapy, including the history, mechanisms, and technologies of pluripotent stem cells, as well as their clinical applications. It also explores the methods, molecular analyses, and clinical applications of in vitro cultures of adipose-derived stem cells. Additionally, the review discusses the past, present, and future of stem cells, as well as their source, potency, and use in regenerative therapies.
The document highlights the clinical applications of stem cell therapy in treating severe heart failure and retinal degenerative diseases. It also discusses the potential use of stem cells for the treatment of neurodegenerative diseases, Achilles tendinopathy, articular cartilage defects, and germ cell specification pathway reconstitution.
The review provides an overview of the current trends of AI in stem cell therapy and its potential applications. It includes a detailed analysis of the use of AI in the identification, classification, and analysis of various types of stem cells, including induced pluripotent stem cells (iPSCs), cardiac progenitor cells, retinal pigment epithelium cells, and neural stem cells.
The document also discusses the use of deep learning-based systems for the automated identification and classification of human embryonic stem cells. It covers various machine learning approaches for the automated quality identification of human iPSC colony images and the predictive identification of neural stem cell differentiation.
In addition to discussing the current trends in AI-based imaging analysis of stem cells, the review also provides a detailed analysis of the methodologies and techniques used in these studies. It includes a discussion of deep learning-based systems for the automated identification and classification of human embryonic stem cells and a machine learning approach to automated quality identification of human iPSC colony images.
The review also covers various machine learning techniques for the automated identification and location analysis of marked stem cell colonies in optical microscopy images. It includes a graph-mining algorithm for automatic detection and counting of embryonic stem cells in fluorescence microscopy images.
Overall, the review provides a comprehensive overview of the current trends and applications of AI in stem cell therapy, with a specific focus on the imaging analysis of stem cells. It highlights the potential of AI-based systems for the automated identification, classification, and analysis of various types of stem cells, and discusses the methodologies and techniques used in these studies.
1489 word summary
Artificial intelligence (AI) has become an increasingly important tool in the analysis and identification of stem cells. This systematic scoping review aimed to map and identify available AI-based techniques for imaging analysis, characterization of stem cell differentiation, and trans-differentiation pathways. Data were collected from five electronic databases and manual citation searching, resulting in 27 studies being included in the review.
Stem cells are unspecialized human cells capable of self-renewal through mitosis, eventually forming more cells. The three categories of stem cell division types consist of induced pluripotent stem cells (iPSCs), embryonic stem cells (ECSs), and adult stem cells (ACSs). The potential for stem cell implementation grows with every experiment, bringing new possibilities for transplantology and regenerative medicine. Stem cell research has targeted a wide variety of diseases, including heart failure, retinal and macular degeneration, neurodegenerative diseases, arthroplasty, fertility disease, and diabetes. Hematopoietic stem cell research has been the most popular due to extensive experimentation and studies over the last fifty years.
The emergence of AI has made stem cells available for selecting suitable medication, establishing a diagnosis, and formulating risks and benefits when it comes to therapy. Machine learning, deep learning, and convolutional neural networks (CNN) have assisted in the reliable detection of various functions, including iPSC colony classifications, non-invasive cell therapy characterizations of normal versus abnormal cells, and image-based cellular morphology.
The search resulted in 27 studies being included in the review, with the majority of published studies occurring in 2021. The United States was the highest contributing country in this field, followed by China, Japan, Brazil, Taiwan, Argentina, Finland, Germany, Italy, South Korea, and Thailand.
The studies tested the power of artificial intelligence in analyzing, identifying, and classifying human or animal iPSC. For example, Fischbacher et al. tested the power of three algorithms in the automatic detection of colony presence and the identification of clonality on approximately 30,000 images. Imamura et al. built an ALS prediction model using a CNN-based deep learning algorithm with 4500 images used for training.
In conclusion, artificial intelligence has the potential to serve as an assisting tool in stem cell imaging. The review aimed to guide and help researchers while planning for future investigations. The use of AI in stem cell research has the potential to revolutionize medicine by providing accurate and efficient analysis of stem cells for various applications.
The use of artificial intelligence (AI) in stem cell research has seen a surge in recent years. Several studies have utilized AI to analyze and classify different types of stem cells, including human-induced pluripotent stem cells (hiPSC), embryonic stem cells (ESC), and other stem cells such as mesenchymal stem cells (MSC) and neural stem cells (NSC). The application of AI in stem cell research has focused on tasks such as cell identification, classification, and segmentation, using various deep learning algorithms and neural networks.
In the context of hiPSC, several studies have demonstrated the effectiveness of AI-based imaging analysis. For example, Chang et al. tested convolutional neural networks (CNN) to trace human iPS cell formation from CD34+ cord blood cells and to automatically detect and localize human iPSC regions in brightfield microscopy images. Orita et al. trained VGG16 using bright-field images of cultured hiPSC-derived cardiomyocytes, while Zhang et al. used XGBoost to model an algorithm for iPS cell identification against mouse embryonic fibroblasts (MEFs) using live-cell images during the early stages of iPSC reprogramming. Kavitha et al. utilized phase-contrast microscopic images of iPSC colonies and five different machine learning algorithms to evaluate iPSC colony features and characterize stem cells. These studies highlight the diverse applications of AI in hiPSC research, ranging from cell formation tracing to colony feature characterization.
In the case of ESC, seven studies have focused on classifying human and animal ESC using different AI algorithms. Guan et al. developed a deep learning method for hESC classification using a dataset of videos, achieving a classification accuracy of 97.23%. Waisman et al. designed an algorithm capable of distinguishing early-differentiating cells from pluripotent cells using the ResNet50 and DenseNET architecture. Theagarajan et al. proposed a system for classifying hESCs into six categories using the CNN approach alone or in combination with Triplet CNN, achieving more than 94% accuracy. These studies demonstrate the potential of AI in accurately classifying ESC based on various differentiation-inducing conditions and categories.
In addition to hiPSC and ESC, other stem cells such as MSC, NSC, and multipotent adult progenitor cells (MAPCs) have also been studied using AI-based imaging analysis. Mota et al. proposed an objective approach for automatically classifying MSC efficacy using a training dataset of images, while Zhu et al. trained and tested different neural networks to recognize the features of differentiated NSCs via brightfield single-cell images. These studies highlight the potential of AI in analyzing and classifying different types of stem cells beyond hiPSC and ESC.
The use of AI in stem cell research has several implications for the field. It has significantly improved the accuracy of image classification tasks, with deep learning technology surpassing human accuracy in some cases. AI has also enabled the segmentation of images at the pixel level, allowing for precise classification within the boundary area. However, proper annotation is still required to achieve this level of effectiveness. AI is at the forefront of accelerating progress in biomedical research and is expected to influence each stage of stem cell studies, ultimately impacting the transfer of research results to clinical practice.
While the amount of research in this field is increasing, there are important factors that investigators should consider when using AI for stem cell analysis. These factors include the quality of images used as a training set, sample size, and the elimination of unexpected events that the algorithm cannot predict. Addressing these factors will be crucial for obtaining reliable and reproducible results in AI-based stem cell research.
In conclusion, the systematic scoping review highlights the diverse applications of AI in stem cell research, particularly in the analysis and classification of hiPSC, ESC, and other types of stem cells using various deep learning algorithms and neural networks. The use of AI has significantly improved the accuracy of image classification tasks and has the potential to impact the transfer of research results to clinical practice. However, addressing important factors such as image quality and sample size will be crucial for obtaining reliable results in AI-based stem cell research.
The document provides a systematic scoping review of the current trends and applications of artificial intelligence (AI) in stem cell therapy, focusing on the imaging analysis of stem cells. The review includes a comprehensive analysis of relevant articles and research studies that utilize AI for the identification, classification, and analysis of stem cells.
The review covers a wide range of topics related to stem cell therapy, including the history, mechanisms, and technologies of pluripotent stem cells, as well as their clinical applications. It also explores the methods, molecular analyses, and clinical applications of in vitro cultures of adipose-derived stem cells. Additionally, the review discusses the past, present, and future of stem cells, as well as their source, potency, and use in regenerative therapies.
The document highlights the clinical applications of stem cell therapy in treating severe heart failure and retinal degenerative diseases. It also discusses the potential use of stem cells for the treatment of neurodegenerative diseases, Achilles tendinopathy, articular cartilage defects, and germ cell specification pathway reconstitution.
The review provides an overview of the current trends of AI in stem cell therapy and its potential applications. It includes a detailed analysis of the use of AI in the identification, classification, and analysis of various types of stem cells, including induced pluripotent stem cells (iPSCs), cardiac progenitor cells, retinal pigment epithelium cells, and neural stem cells.
The document also discusses the use of deep learning-based systems for the automated identification and classification of human embryonic stem cells. It covers various machine learning approaches for the automated quality identification of human iPSC colony images and the predictive identification of neural stem cell differentiation.
In addition to discussing the current trends in AI-based imaging analysis of stem cells, the review also provides a detailed analysis of the methodologies and techniques used in these studies. It includes a discussion of deep learning-based systems for the automated identification and classification of human embryonic stem cells and a machine learning approach to automated quality identification of human iPSC colony images.
The review also covers various machine learning techniques for the automated identification and location analysis of marked stem cell colonies in optical microscopy images. It includes a graph-mining algorithm for automatic detection and counting of embryonic stem cells in fluorescence microscopy images.
Overall, the review provides a comprehensive overview of the current trends and applications of AI in stem cell therapy, with a specific focus on the imaging analysis of stem cells. It highlights the potential of AI-based systems for the automated identification, classification, and analysis of various types of stem cells, and discusses the methodologies and techniques used in these studies.