Department of Internal Medicine, Thai Nguyen University of Medicine and Pharmacy, Thai Nguyen, Vietnam.
Department of Nephro-Urology and Dialysis, Thai Nguyen National Hospital, Thai Nguyen, Vietnam.
BMC Nephrol. 2024 Oct 16;25(1):353. doi: 10.1186/s12882-024-03793-7.
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.
急性肾损伤（AKI）是一个重大的临床挑战，因为它会迅速发展为肾衰竭，导致严重的并发症，如电解质失衡、液体超负荷，以及可能需要肾脏替代治疗。早期发现和预测 AKI 可以通过及时干预来改善患者的预后。本综述是一篇叙述性文献综述，旨在探讨 AKI 的早期检测和预测的最新模型。我们对来自不同研究的结果进行了全面的综述，突出了它们的优势、局限性以及在医疗保健环境中实施的实际考虑因素。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。本文还探讨了 AI 在 AKI 检测和预测方面的进展，涉及到其临床适用性、挑战和常规实施的机会。
