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机器学习在肾病变中的应用:最新研究综述。

Machine Learning for Renal Pathologies: An Updated Survey.

机构信息

Department of Industrial Engineering, University of Florence, 50139 Florence, Italy.

出版信息

Sensors (Basel). 2022 Jul 1;22(13):4989. doi: 10.3390/s22134989.

Abstract

Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available. Based on the study conducted, it is possible to see a growing trend and interest in the use of machine learning applications in nephrology, becoming an additional tool for physicians, which can enable them to make more accurate and faster diagnoses, although there remains a major limitation given the difficulty in creating public databases that can be used by the scientific community to corroborate and eventually make a positive contribution in this area.

摘要

在医学领域应用现代机器学习技术的文献中,人们对将这些技术应用于肾病领域越来越感兴趣,特别是在研究肾脏病理方面,因为这些疾病在我们的社会中非常普遍和广泛,影响了很大比例的人群,并导致了各种并发症,在某些情况下甚至导致死亡。出于这些原因,作者认为有必要使用其中一个主要的文献数据库来收集并分析截至 2022 年 2 月在肾病领域应用机器学习技术的研究,根据所涉及的病理将其分组:肾肿瘤、急性肾损伤、慢性肾脏病、肾结石、肾小球疾病、肾移植和其他较少见的疾病。在总共 224 项研究中,根据纳入和排除标准,对其中 59 项研究进行了分析,同时考虑了所使用的方法和可用的数据类型。基于进行的研究,可以看到机器学习应用在肾病学中的应用呈增长趋势,并且越来越受到关注,成为医生的额外工具,可以使他们更快更准确地做出诊断,尽管仍然存在一个主要的局限性,即难以创建可供科学界使用的公共数据库,以在该领域进行佐证并最终做出积极贡献。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d59/9269842/a984e1930292/sensors-22-04989-g001.jpg

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