{"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。
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.
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.