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纳米技术与人工智能在血脂异常管理 - 心血管疾病中的应用:进展、挑战与未来展望

Nanotechnology and Artificial Intelligence in Dyslipidemia Management-Cardiovascular Disease: Advances, Challenges, and Future Perspectives.

作者信息

Młynarska Ewelina, Bojdo Kinga, Frankenstein Hanna, Kustosik Natalia, Mstowska Weronika, Przybylak Aleksandra, Rysz Jacek, Franczyk Beata

机构信息

Department of Nephrocardiology, Medical University of Lodz, 90-549 Łódź, Poland.

Department of Nephrology, Hypertension and Internal Medicine, Medical University of Lodz, 90-549 Łodz, Poland.

出版信息

J Clin Med. 2025 Jan 29;14(3):887. doi: 10.3390/jcm14030887.

Abstract

This narrative review explores emerging technologies in dyslipidemia management, focusing on nanotechnology and artificial intelligence (AI). It examines the current treatment recommendations and contrasts them with the future prospects enabled by these innovations. Nanotechnology shows significant potential in enhancing drug delivery systems, enabling more targeted and efficient lipid-lowering therapies. In parallel, AI offers advancements in diagnostics, cardiovascular risk prediction, and personalized treatment strategies. AI-based decision support systems and machine learning algorithms are particularly promising for analyzing large datasets and delivering evidence-based recommendations. Together, these technologies hold the potential to revolutionize dyslipidemia management, improving outcomes and optimizing patient care. In addition, this review covers key topics such as cardiovascular disease biomarkers and risk factors, providing insights into the current methods for assessing cardiovascular risk. It also discusses the current understanding of dyslipidemia, including pathophysiology and clinical management. Together, these insights and technologies hold the potential to revolutionize dyslipidemia management, improving outcomes and optimizing patient care.

摘要

这篇叙述性综述探讨了血脂异常管理中的新兴技术,重点关注纳米技术和人工智能(AI)。它审视了当前的治疗建议,并将其与这些创新所带来的未来前景进行对比。纳米技术在增强药物递送系统方面显示出巨大潜力,能够实现更具针对性和高效的降脂治疗。与此同时,人工智能在诊断、心血管疾病风险预测和个性化治疗策略方面取得了进展。基于人工智能的决策支持系统和机器学习算法在分析大型数据集并提供循证建议方面尤其具有前景。这些技术共同具有变革血脂异常管理的潜力,可改善治疗结果并优化患者护理。此外,本综述涵盖了心血管疾病生物标志物和风险因素等关键主题,深入探讨了当前评估心血管疾病风险的方法。它还讨论了目前对血脂异常的认识,包括病理生理学和临床管理。这些见解和技术共同具有变革血脂异常管理的潜力,可改善治疗结果并优化患者护理。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e658/11818864/e24db1fd6d44/jcm-14-00887-g001.jpg

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