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探索人工智能在干细胞治疗中的当前趋势:一项系统综述。

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

作者信息

Srinivasan Mirra, Thangaraj Santhosh Raja, Ramasubramanian Krishnamurthy, Thangaraj Padma Pradha, Ramasubramanian Krishna Vyas

机构信息

Internal Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA.

Internal Medicine, Rajah Muthiah Medical College, Chidambaram, IND.

出版信息

Cureus. 2021 Dec 1;13(12):e20083. doi: 10.7759/cureus.20083. eCollection 2021 Dec.

DOI:10.7759/cureus.20083
PMID:34873560
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8635466/
Abstract

The concept of 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 future of regenerative medicine.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de32/8635466/10b4b9f06ea3/cureus-0013-00000020083-i06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de32/8635466/f87f121dd476/cureus-0013-00000020083-i01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de32/8635466/6176b42413b8/cureus-0013-00000020083-i02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de32/8635466/7eec9267ef1b/cureus-0013-00000020083-i03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de32/8635466/0a9afda6d90e/cureus-0013-00000020083-i04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de32/8635466/b3d43cb91673/cureus-0013-00000020083-i05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de32/8635466/10b4b9f06ea3/cureus-0013-00000020083-i06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de32/8635466/f87f121dd476/cureus-0013-00000020083-i01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de32/8635466/6176b42413b8/cureus-0013-00000020083-i02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de32/8635466/7eec9267ef1b/cureus-0013-00000020083-i03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de32/8635466/0a9afda6d90e/cureus-0013-00000020083-i04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de32/8635466/b3d43cb91673/cureus-0013-00000020083-i05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de32/8635466/10b4b9f06ea3/cureus-0013-00000020083-i06.jpg
摘要

随着科学家和研究人员对再生医学的追求,这一概念正在呈现新的形式。到目前为止,医生一直通过治疗症状来“应对”疾病;然而,现代医学正在向再生方向转变,而非反应性治疗,干细胞疗法就在此发挥作用——即用功能更佳的全新细胞替代受损细胞的概念。干细胞治疗目前正用于治疗自身免疫性、炎症性、神经学、矫形外科学和创伤性疾病,针对多种疾病的各类研究也在开展。它也可能是抗衰老和无病状态的答案。尽管有诸多益处,但用干细胞治疗患者时可能存在大量失误。随着现代技术和研究的进步,医学开始转向人工智能(AI),以解决再生医学中可能出现的复杂失误。为实现成功治疗,在分析具备天然细胞所有特性的健康且有活性的干细胞时,必须做到精准和准确。本综述旨在探讨和研究人工智能在干细胞治疗中的应用,以及它如何影响医学实践方式,从而开辟一条通往副作用可忽略不计的再生未来之路。以下数据库用于文献检索:PubMed、谷歌学术、PubMed Central和电气与电子工程师协会(IEEE)Xplore。经过全面分析,根据本综述作者设定的纳入和排除标准选取研究,这些标准包括过去六年内发表的英文报告。作者还确保纳入使用适当质量评估工具评估后符合每份报告质量要求的研究,之后发现八份报告符合条件并纳入本综述。本研究主要围绕机器学习、深度神经网络(DNN)以及这些类别中包含的人工智能其他子类展开。虽然在干细胞治疗中应用各种人工智能手段存在担忧和局限性,但对符合条件的研究分析得出结论,人工智能在诸多方面为全球医疗生态系统带来显著益处,比如确定干细胞的活力、功能、生物安全性和生物有效性,以及进行合适的患者选择。将人工智能应用于这一新技术带来了再生医学的精准性、准确性和一场变革。此外目前干细胞治疗未获美国食品药品监督管理局(FDA)批准(造血干细胞除外),迄今为止,它被视为实验性治疗,风险和益处尚无明确概述。鉴于这一局限性,世界各地定期开展研究,期望得出具体结论,像人工智能这样的技术进步有助于塑造再生医学的未来。

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