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非增殖性糖尿病视网膜病变主要低灌注和高灌注阶段的特征及自动鉴别

Characterization and Automatic Discrimination between Predominant Hypoperfusion and Hyperperfusion Stages of NPDR.

作者信息

Mendes Luís, Ribeiro Luísa, Marques Inês, Lobo Conceição, Cunha-Vaz José

机构信息

AIBILI-Association for Innovation and Biomedical Research on Light and Image, 3000-548 Coimbra, Portugal.

出版信息

J Pers Med. 2024 Sep 14;14(9):977. doi: 10.3390/jpm14090977.

Abstract

BACKGROUND/OBJECTIVES: Diabetic retinopathy (DR) is a common diabetes complication that can lead to blindness through vision-threatening complications like clinically significant macular edema and proliferative retinopathy. Identifying eyes at risk of progression using non-invasive methods could help develop targeted therapies to halt diabetic retinal disease progression.

METHODS

A set of 82 imaging and systemic features was used to characterize the progression of nonproliferative diabetic retinopathy (NPDR). These features include baseline measurements (static features) and those capturing the temporal dynamic behavior of these static features within one year (dynamic features). Interpretable models were trained to distinguish between eyes with Early Treatment Diabetic Retinopathy Study (ETDRS) level 35 and eyes with ETDRS levels 43-47. The data used in this research were collected from 109 diabetic type 2 patients (67.26 ± 2.70 years; diabetes duration 19.6 ± 7.26 years) and acquired over 2 years.

RESULTS

The characterization of the data indicates that NPDR progresses from an initial stage of hypoperfusion to a hyperperfusion response. The performance of the classification model using static features achieved an area under the curve (AUC) of the receiver operating characteristics equal to 0.84 ± 0.07, while the model using both static and dynamic features achieved an AUC of 0.91 ± 0.05.

CONCLUSION

NPDR progresses through an initial hypoperfusion stage followed by a hyperperfusion response. Characterizing and automatically identifying this disease progression stage is valuable and necessary. The results indicate that achieving this goal is feasible, paving the way for the improved evaluation of progression risk and the development of better-targeted therapies to prevent vision-threatening complications.

摘要

背景/目的:糖尿病视网膜病变(DR)是一种常见的糖尿病并发症,可通过如临床显著性黄斑水肿和增殖性视网膜病变等威胁视力的并发症导致失明。使用非侵入性方法识别有进展风险的眼睛有助于开发针对性疗法以阻止糖尿病视网膜疾病的进展。

方法

一组82个影像学和全身特征用于描述非增殖性糖尿病视网膜病变(NPDR)的进展。这些特征包括基线测量值(静态特征)以及那些捕捉这些静态特征在一年内的时间动态行为的特征(动态特征)。训练可解释模型以区分糖尿病视网膜病变早期治疗研究(ETDRS)35级的眼睛和ETDRS 43 - 47级的眼睛。本研究中使用的数据收集自109名2型糖尿病患者(67.26 ± 2.70岁;糖尿病病程19.6 ± 7.26年),并在两年多的时间里获取。

结果

数据特征表明NPDR从最初的灌注不足阶段发展到高灌注反应阶段。使用静态特征的分类模型在接收器操作特征曲线下面积(AUC)为0.84 ± 0.07,而使用静态和动态特征的模型AUC为0.91 ± 0.05。

结论

NPDR经历一个初始的灌注不足阶段,随后是高灌注反应阶段。表征并自动识别该疾病进展阶段是有价值且必要的。结果表明实现这一目标是可行的,为改善进展风险评估以及开发更好的针对性疗法以预防威胁视力的并发症铺平了道路。

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