Exploring the Current Trends of Artificial Intelligence in Stem Cell Therapy: A Systematic Review

Abstract

The concept of healing in medicine has been taking a new form where scientists and researchers are in pursuance of regenerative medicine. Until now, doctors have "reacted" to disease by treating the symptoms; however, modern medicine is transforming toward regeneration rather than reactive treatment, which is where stem cell therapy comes into the play-the concept of replacing damaged cells with brand new cells that perform the same function better. Stem cell treatment is currently being used to treat autoimmune, inflammatory, neurological, orthopedic, and traumatic disorders, with various research being undertaken for a wide range of diseases. It could also be the answer to anti-aging and a disease-free state. Despite the benefits, numerous errors could prevail in treating patients with stem cells. With the advancement of technology and research in the modern period, medicine is beginning to turn to artificial intelligence (AI) to address the complicated errors that could occur in regenerative medicine. For successful treatment, one must achieve precision and accuracy when analyzing healthy and productive stem cells that possess all the properties of a native cell. This review intends to discuss and study the application of AI in stem cell therapy and how it influences how medicine is practiced, thus creating a path to a regenerative future with negligible adverse effects. The following databases were used for a literature search: PubMed, Google Scholar, PubMed Central, and Institute of Electrical and Electronics Engineers (IEEE) Xplore. After a thorough analysis, studies were chosen, keeping in mind the inclusion and exclusion criteria set by the authors of this review, which comprised reports published within the last six years in the English language. The authors also made sure to include studies that sufficed the quality of each report assessed using appropriate quality appraisal tools, after which eight reports were found to be eligible and were included in this review. This research mainly revolves around machine learning, deep neural networks (DNN), and other subclasses of AI encompassed in these categories. While there are concerns and limitations in implementing various mediums of AI in stem cell therapy, the analysis of the eligible studies concluded that artificial intelligence provides significant benefits to the global healthcare ecosystem in numerous ways, such as determining the viability, functionality, biosafety, and bioefficacy of stem cells, as well as appropriate patient selection. Applying AI to this novelty brings out the precision, accuracy, and a revolution in regenerative medicine. In addition, stem cell therapy is not currently FDA approved (except for the blood-forming stem cells) and, to date, is considered experimental with no clear outline of risks and benefits. Given this limitation, studies are conducted regularly around the world in hopes for a concrete conclusion where technological advances such as AI could help in shaping the ...

Digital Technologies: Advancing Individualized Treatments through Gene and Cell Therapies, Pharmacogenetics, and Disease Detection and Diagnostics

“…For instance, advanced computational models have collated millions of possible protein combinations into a catalog that could help target specific cell types in vivo [ 31 ] and provide predictions of the mortality risk associated with cell transplantation [ 32 ]. Digital technologies can also help determine cell viability, functionality, bioefficacy, and appropriate patient selection for cell therapy [ 33 ]. Another cell therapy platform is the organoid.…”

Digital technologies: advancing individualized treatments through gene and cell therapies, pharmacogenetics, and disease detection and diagnostics

“…For instance, advanced computational models have collated millions of possible protein combinations into a catalog that could help target specific cell types in vivo (Dannenfelser et al, 2020) and provide predictions of mortality risk associated with cell transplantation (Shende and Devlekar, 2021). Digital technologies can also help determine cell viability, functionality, bioefficacy, and appropriate patient selection for cell therapy (Srinivasan et al, 2021). Another cell therapy platform is the organoid.…”

Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review

“…The emergence of stem cell research began in the year 1961, which marked the discovery of bone multipotent stem cells [ 1 ]. After several decades, the human pluripotent stem cells were first used in the preclinical stages of research which included isolating cells, implementing their functions, identifying their roles, and applying animal trials (such as injecting human-derived cardiomyocytes in damaged rodent hearts and witnessing their improvements) [ 2 , 3 ]. In the past few years, a trend in stem cell research has exponentially increased as it passed to the clinical stages, where the advancement of technology has enabled such innovation to potentially transition into human clinical trials in the future [ 2 , 3 ].…”

“…The revolutionary advancements in technology, underlined by artificial intelligence (AI), have made stem cells available in terms of selecting the most suitable medication, establishing a diagnosis, and formulating risks and benefits when it comes to therapy [ 20 ]. In particular, emerging techniques of machine learning, deep learning, and convolutional neural networks (CNN) have assisted the framework of the reliable detection for various functions, including iPSC colony classifications [ 2 ], non-invasive cell therapy characterizations of normal versus abnormal cells [ 2 ], and image-based cellular morphology [ 2 ]. The accessibility of a wide variety of medical images combined with the continuously developing technology in the field of AI will take medicine to a whole different level [ 2 ].…”

“…In particular, emerging techniques of machine learning, deep learning, and convolutional neural networks (CNN) have assisted the framework of the reliable detection for various functions, including iPSC colony classifications [ 2 ], non-invasive cell therapy characterizations of normal versus abnormal cells [ 2 ], and image-based cellular morphology [ 2 ]. The accessibility of a wide variety of medical images combined with the continuously developing technology in the field of AI will take medicine to a whole different level [ 2 ]. The goal of the current research is to map and identify the available AI-based techniques for the imaging analysis of stem cells, the characterization of stem cell differentiation, and trans-differentiation pathways.…”