“嗨,我能帮你什么?”:在肾脏研究中拥抱人工智能。

IF 3.7 2区 医学 Q1 PHYSIOLOGY
Anita T Layton
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引用次数: 0

摘要

近年来,生物学和精准医学在生成大规模分子和生物医学数据集以及使用先进的机器学习算法分析这些数据方面取得了重大进展。机器学习在肾脏生理学和病理生理学中的应用包括从成像数据中分割肾脏结构,并使用电子健康记录预测急性肾损伤或慢性肾脏疾病等情况。尽管机器学习有可能通过提供创新的诊断和治疗工具来彻底改变肾脏学,但它在肾脏研究中的应用速度比在其他器官系统中慢。造成这种利用不足的因素有几个。肾脏作为一个器官的复杂性,具有复杂的生理学和专门的细胞群,使得将大量组学数据外推到特定过程中具有挑战性。此外,肾脏疾病往往表现为重叠的表现和形态学变化,使诊断和治疗变得复杂。此外,与其他疾病相比,肾脏疾病获得的资金较少,导致人们的认识较低,公私伙伴关系有限。为了促进机器学习在肾脏研究中的应用,这篇综述介绍了机器学习,并回顾了它在肾脏研究方面的显著应用,如形态学分析、组学数据检查以及疾病诊断和预后。还讨论了与数据驱动预测技术相关的挑战和局限性。这篇综述的目的是提高人们的认识,并鼓励肾脏研究界将机器学习作为一种强大的工具,推动理解肾脏疾病和改善患者护理的进步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
"Hi, how can i help you?": embracing artificial intelligence in kidney research.

In recent years, biology and precision medicine have benefited from major advancements in generating large-scale molecular and biomedical datasets and in analyzing those data using advanced machine learning algorithms. Machine learning applications in kidney physiology and pathophysiology include segmenting kidney structures from imaging data and predicting conditions like acute kidney injury or chronic kidney disease using electronic health records. Despite the potential of machine learning to revolutionize nephrology by providing innovative diagnostic and therapeutic tools, its adoption in kidney research has been slower than in other organ systems. Several factors contribute to this underutilization. The complexity of the kidney as an organ, with intricate physiology and specialized cell populations, makes it challenging to extrapolate bulk omics data to specific processes. In addition, kidney diseases often present with overlapping manifestations and morphological changes, making diagnosis and treatment complex. Moreover, kidney diseases receive less funding compared with other pathologies, leading to lower awareness and limited public-private partnerships. To promote the use of machine learning in kidney research, this review provides an introduction to machine learning and reviews its notable applications in renal research, such as morphological analysis, omics data examination, and disease diagnosis and prognosis. Challenges and limitations associated with data-driven predictive techniques are also discussed. The goal of this review is to raise awareness and encourage the kidney research community to embrace machine learning as a powerful tool that can drive advancements in understanding kidney diseases and improving patient care.

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来源期刊
CiteScore
8.40
自引率
7.10%
发文量
154
审稿时长
2-4 weeks
期刊介绍: The American Journal of Physiology - Renal Physiology publishes original manuscripts on timely topics in both basic science and clinical research. Published articles address a broad range of subjects relating to the kidney and urinary tract, and may involve human or animal models, individual cell types, and isolated membrane systems. Also covered are the pathophysiological basis of renal disease processes, regulation of body fluids, and clinical research that provides mechanistic insights. Studies of renal function may be conducted using a wide range of approaches, such as biochemistry, immunology, genetics, mathematical modeling, molecular biology, as well as physiological and clinical methodologies.
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