用于早期检测急性肾损伤的人工智能和预测模型:改变临床实践。

IF 2.2 4区 医学 Q2 UROLOGY & NEPHROLOGY
Tu T Tran, Giae Yun, Sejoong Kim
{"title":"用于早期检测急性肾损伤的人工智能和预测模型:改变临床实践。","authors":"Tu T Tran, Giae Yun, Sejoong Kim","doi":"10.1186/s12882-024-03793-7","DOIUrl":null,"url":null,"abstract":"<p><p>Acute kidney injury (AKI) presents a significant clinical challenge due to its rapid progression to kidney failure, resulting in serious complications such as electrolyte imbalances, fluid overload, and the potential need for renal replacement therapy. Early detection and prediction of AKI can improve patient outcomes through timely interventions. This review was conducted as a narrative literature review, aiming to explore state-of-the-art models for early detection and prediction of AKI. We conducted a comprehensive review of findings from various studies, highlighting their strengths, limitations, and practical considerations for implementation in healthcare settings. We highlight the potential benefits and challenges of their integration into routine clinical care and emphasize the importance of establishing robust early-detection systems before the introduction of artificial intelligence (AI)-assisted prediction models. Advances in AI for AKI detection and prediction are examined, addressing their clinical applicability, challenges, and opportunities for routine implementation.</p>","PeriodicalId":9089,"journal":{"name":"BMC Nephrology","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11484428/pdf/","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence and predictive models for early detection of acute kidney injury: transforming clinical practice.\",\"authors\":\"Tu T Tran, Giae Yun, Sejoong Kim\",\"doi\":\"10.1186/s12882-024-03793-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Acute kidney injury (AKI) presents a significant clinical challenge due to its rapid progression to kidney failure, resulting in serious complications such as electrolyte imbalances, fluid overload, and the potential need for renal replacement therapy. Early detection and prediction of AKI can improve patient outcomes through timely interventions. This review was conducted as a narrative literature review, aiming to explore state-of-the-art models for early detection and prediction of AKI. We conducted a comprehensive review of findings from various studies, highlighting their strengths, limitations, and practical considerations for implementation in healthcare settings. We highlight the potential benefits and challenges of their integration into routine clinical care and emphasize the importance of establishing robust early-detection systems before the introduction of artificial intelligence (AI)-assisted prediction models. Advances in AI for AKI detection and prediction are examined, addressing their clinical applicability, challenges, and opportunities for routine implementation.</p>\",\"PeriodicalId\":9089,\"journal\":{\"name\":\"BMC Nephrology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11484428/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Nephrology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12882-024-03793-7\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"UROLOGY & NEPHROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Nephrology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12882-024-03793-7","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"UROLOGY & NEPHROLOGY","Score":null,"Total":0}
引用次数: 0

摘要

急性肾损伤(AKI)是一项重大的临床挑战,因为它会迅速发展为肾衰竭,导致电解质失衡、体液超负荷等严重并发症,并可能需要进行肾脏替代治疗。早期发现和预测 AKI 可通过及时干预改善患者预后。本综述以叙述性文献综述的形式进行,旨在探索早期检测和预测 AKI 的最先进模型。我们对各种研究结果进行了全面回顾,强调了它们的优势、局限性以及在医疗机构实施时的实际考虑因素。我们强调了将其纳入常规临床护理的潜在益处和挑战,并强调了在引入人工智能(AI)辅助预测模型之前建立强大的早期检测系统的重要性。我们研究了人工智能在 AKI 检测和预测方面的进展,探讨了其临床适用性、挑战以及常规实施的机遇。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial intelligence and predictive models for early detection of acute kidney injury: transforming clinical practice.

Acute kidney injury (AKI) presents a significant clinical challenge due to its rapid progression to kidney failure, resulting in serious complications such as electrolyte imbalances, fluid overload, and the potential need for renal replacement therapy. Early detection and prediction of AKI can improve patient outcomes through timely interventions. This review was conducted as a narrative literature review, aiming to explore state-of-the-art models for early detection and prediction of AKI. We conducted a comprehensive review of findings from various studies, highlighting their strengths, limitations, and practical considerations for implementation in healthcare settings. We highlight the potential benefits and challenges of their integration into routine clinical care and emphasize the importance of establishing robust early-detection systems before the introduction of artificial intelligence (AI)-assisted prediction models. Advances in AI for AKI detection and prediction are examined, addressing their clinical applicability, challenges, and opportunities for routine implementation.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
BMC Nephrology
BMC Nephrology UROLOGY & NEPHROLOGY-
CiteScore
4.30
自引率
0.00%
发文量
375
审稿时长
3-8 weeks
期刊介绍: BMC Nephrology is an open access journal publishing original peer-reviewed research articles in all aspects of the prevention, diagnosis and management of kidney and associated disorders, as well as related molecular genetics, pathophysiology, and epidemiology.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信