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基于彩色眼底摄影和人工智能对不同糖代谢状态人群视网膜血管结构参数的比较分析

Comparative analysis of retinal vascular structural parameters in populations with different glucose metabolism status based on color fundus photography and artificial intelligence.

作者信息

Chen Naimei, Zhu Zhentao, Gong Di, Xu Xinrong, Hu Xinya, Yang Weihua

机构信息

Department of Ophthalmology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China.

Department of Ophthalmology, Huaian Hospital of Huaian City, Huaian, China.

出版信息

Front Cell Dev Biol. 2025 Feb 3;13:1550176. doi: 10.3389/fcell.2025.1550176. eCollection 2025.

Abstract

OBJECTIVE

Measure and analyze retinal vascular parameters in individuals with varying glucose metabolism, explore preclinical retinal microstructure changes related to diabetic retinopathy (DR), and assess glucose metabolism's impact on retinal structure.

METHODS

The study employed a cross-sectional design encompassing a 4-year period from 2020 to 2024. Fundus photographs from 320 individuals (2020-2024) were categorized into non-diabetes, pre-diabetes, type 2 diabetes mellitus (T2DM) without DR, and T2DM with mild-to-moderate non-proliferative DR (NPDR) groups. An artificial intelligence (AI)-based automatic measurement system was used to quantify retinal blood vessels in the fundus color photographic images, enabling inter-group parameter comparison and analysis of significant differences.

RESULTS

Between January 2020 and June 2024, fundus color photographs were collected from 320 individuals and categorized into four groups: non-diabetes (n = 54), pre-diabetes (n = 71), T2DM without overt DR (n = 144), and T2DM with mild-to-moderate NPDR (n = 51). In pairwise comparisons among individuals with pre-diabetes, T2DM without DR, and T2DM with mild-to-moderate NPDR. Fasting blood glucose (FBG), glycated hemoglobin (HbA1c), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were significantly different ( < 0.05). Within the T2DM population, FBG, HbA1c, age, SBP, and DBP were significant predictors for mild-to-moderate NPDR ( < 0.05). Average venous branching number (branch_avg_v) was significantly different among pre-diabetes, T2DM without DR, and T2DM with mild-to-moderate NPDR groups. In patients with T2DM with mild-to-moderate NPDR, Average length of arteries (length_avg_a) and average length of veins (length_avg_v) increased, whereas branch_avg_v, average venous branching angle (angle_avg_v), average venous branching asymmetry (asymmetry_avg_v),overall length density (vessel_length_density), and vessel area density (vessel_density) decreased significantly ( < 0.05). Logistic regression analysis identified length_avg_a, branch_avg_v, angle_avg_v, asymmetry_avg_v, vessel_length_density, and vessel_density as independent predictors of mild-to-moderate NPDR in patients with T2DM. Receiver Operating Characteristic (ROC) curve analysis demonstrated that length_avg_a, length_avg_v, branch_avg_v, angle_avg_v, asymmetry_avg_v, vessel_length_density, and vessel_density had diagnostic value for mild-to-moderate NPDR ( < 0.05).

CONCLUSION

In individuals diagnosed with T2DM, specific retinal vascular parameters, such as branch_avg_v and vessel_density, demonstrate a significant correlation with mild-to-moderate NPDR. These parameters hold promise as preclinical biomarkers for detecting vascular abnormalities associated with DR.

摘要

目的

测量并分析不同糖代谢状态个体的视网膜血管参数,探索与糖尿病视网膜病变(DR)相关的临床前期视网膜微观结构变化,并评估糖代谢对视网膜结构的影响。

方法

本研究采用横断面设计,涵盖2020年至2024年的4年时间。将320名个体(2020 - 2024年)的眼底照片分为非糖尿病、糖尿病前期、无DR的2型糖尿病(T2DM)以及伴有轻度至中度非增殖性DR(NPDR)的T2DM组。使用基于人工智能(AI)的自动测量系统对眼底彩色照片图像中的视网膜血管进行量化,以便进行组间参数比较和显著差异分析。

结果

2020年1月至2024年6月期间,收集了320名个体的眼底彩色照片,并分为四组:非糖尿病(n = 54)、糖尿病前期(n = 71)、无明显DR的T2DM(n = 144)以及伴有轻度至中度NPDR的T2DM(n = 51)。在糖尿病前期、无DR的T2DM以及伴有轻度至中度NPDR的个体之间进行两两比较。空腹血糖(FBG)、糖化血红蛋白(HbA1c)、收缩压(SBP)和舒张压(DBP)存在显著差异(<0.05)。在T2DM人群中,FBG、HbA1c、年龄、SBP和DBP是轻度至中度NPDR的显著预测因素(<0.05)。糖尿病前期、无DR的T2DM以及伴有轻度至中度NPDR的组间平均静脉分支数(branch_avg_v)存在显著差异。在伴有轻度至中度NPDR的T2DM患者中,动脉平均长度(length_avg_a)和静脉平均长度(length_avg_v)增加,而branch_avg_v、平均静脉分支角度(angle_avg_v)、平均静脉分支不对称性(asymmetry_avg_v)、总长度密度(vessel_length_density)和血管面积密度(vessel_density)显著降低(<0.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9ec/11830720/5971ae488290/fcell-13-1550176-g001.jpg

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