■慢性肾脏病(CKD)是一个重要的全球健康问题,强调早期发现的必要性,以促进及时的临床干预。利用视网膜的独特能力提供对全身血管健康的见解,它变得很有趣,早期CKD检测的非侵入性选择。将这种方法与现有的侵入性方法相结合,可以全面了解患者的健康状况。提高诊断的准确性和治疗的有效性。
■这篇综述的目的是批判性地评估视网膜成像作为基于视网膜血管变化的CKD检测的诊断工具的潜力。该评论跟踪了从传统的手动评估到最新的深度学习的演变。
■对文献进行了全面审查,使用有针对性的数据库搜索和三步方法进行文章评估:识别,筛选,以及基于Prisma指南的包容性。重点是关于视网膜成像检测CKD的独特和新的研究。最初发现的457种出版物总共有70种,符合我们的纳入标准,因此进行了分析。在包括的70项研究中,35例糖尿病视网膜病变与CKD的相关性,23以通过视网膜成像检测CKD为中心,四个人试图通过人工智能和视网膜成像的结合来自动化检测。
■显著的视网膜特征,如小动脉狭窄,静脉扩张,特定的视网膜病变标志物(如微动脉瘤,出血,和渗出物),动静脉比值(AVR)的变化与CKD进展密切相关。我们还发现,将深度学习与视网膜成像相结合用于CKD检测可以提供非常有前途的途径。因此,通过这种技术利用视网膜成像有望提高CKD检测系统的精度和预后能力,提供一种可以改变患者护理实践的非侵入性诊断替代方案。
■总之,视网膜成像作为CKD的诊断工具具有很高的潜力,因为它是非侵入性的,通过可观察的微血管变化促进早期检测,提供肾脏健康的预测性见解,and,当与深度学习算法配对时,提高CKD筛查的准确性和有效性。
UNASSIGNED: Chronic kidney disease (CKD) is a significant global health concern, emphasizing the necessity of early detection to facilitate prompt clinical intervention. Leveraging the unique ability of the retina to offer insights into systemic vascular health, it emerges as an interesting, non-invasive option for early CKD detection. Integrating this approach with existing invasive methods could provide a comprehensive understanding of patient health, enhancing diagnostic accuracy and treatment effectiveness.
UNASSIGNED: The purpose of this
review is to critically assess the potential of retinal imaging to serve as a diagnostic tool for CKD detection based on retinal vascular changes. The
review tracks the evolution from conventional manual evaluations to the latest state-of-the-art in deep learning.
UNASSIGNED: A comprehensive examination of the literature was carried out, using targeted database searches and a three-step methodology for article evaluation: identification, screening, and inclusion based on Prisma guidelines. Priority was given to unique and new research concerning the detection of CKD with retinal imaging. A total of 70 publications from 457 that were initially discovered satisfied our inclusion criteria and were thus subjected to analysis. Out of the 70 studies included, 35 investigated the correlation between diabetic retinopathy and CKD, 23 centered on the detection of CKD via retinal imaging, and four attempted to automate the detection through the combination of artificial intelligence and retinal imaging.
UNASSIGNED: Significant retinal features such as arteriolar narrowing, venular widening, specific retinopathy markers (like microaneurysms, hemorrhages, and exudates), and changes in arteriovenous ratio (AVR) have shown strong correlations with CKD progression. We also found that the combination of deep learning with retinal imaging for CKD detection could provide a very promising pathway. Accordingly, leveraging retinal imaging through this technique is expected to enhance the precision and prognostic capacity of the CKD detection system, offering a non-invasive diagnostic alternative that could transform patient care practices.
UNASSIGNED: In summary, retinal imaging holds high potential as a diagnostic tool for CKD because it is non-invasive, facilitates early detection through observable microvascular changes, offers predictive insights into renal health, and, when paired with deep learning algorithms, enhances the accuracy and effectiveness of CKD screening.