摘要
目的 基于人工智能技术对视网膜血管形态学参数进行全自动定量测量,分析我国北方50岁以上人群视网膜血管参数及分布特征.方法 采用横断面研究方法,纳入2011年1月至2021年12月就诊于北京同仁医院的50岁以上无眼底病的患者1 842例,对纳入的受试者进行标准化问卷调查、抽血和眼科检查;收集各受试者任意一眼以视盘为中心的彩色眼底照片,采用基于深度学习的语义分割网络ResNet101-Unet构建血管分割模型,进行全自动视网膜血管参数定量测量,主要测量指标包括视网膜血管分支夹角、血管分形维数、血管平均管径和血管平均弯曲度.比较不同性别间各视网膜参数的差异.采用多元线性回归分析法分析最佳矫正视力、眼压、眼轴长度等眼部因素和性别、年龄、高血压、糖尿病、心血管疾病等全身因素是否是各视网膜血管参数的影响因素.结果 模型对于血管分割和视盘分割的准确度均高于0.95.1 842例受试者血管分支夹角为(51.023±11.623)°;血管分形维数为1.573(1.542,1.592);血管平均管径为64.124(60.814,69.053)μm;血管平均弯曲度为(0.001 062±0.000 165)°.男性血管分支夹角大于女性,血管平均管径和血管平均弯曲度小于女性,差异均有统计学意义(均P＜0.05).全身因素多元线性回归分析结果显示,患有心血管疾病的人群较无心血管疾病的人群血管平均管径增大1.142 μm(B=1.142,P=0.029,95％CI:0.116～2.167);血管平均弯曲度与高血压(B=3.053×10-5,P=0.002,95％CI:1.167×10-5～4.934× 10-5)和饮酒量(B=1.036x10-5,P=0.014,95％CI:0.211×10-5～1.860×10-5)呈正相关,与高脂血症呈负相关(B=-2.422×10-5,P=0.015,95％CI:-4.382×10-5～-0.462x10-5).眼部因素多元线性回归分析结果显示,眼轴长度每增加1 mm,血管分形维数减小0.004(B=-0.004,P＜0.001,95％CI:-0.006～-0.002),血管平均管径减小 0.266 μm(B=-0.266,P=0.037,95％CI:-0.516～-0.016),血管平均弯曲度减小-2.45×10-5°(B=-2.45×10-5,P＜0.001,95％CI:-0.313×10-5～-0.177×10-5).BCVA 每增加 1.0,血管分支夹角增大 3.992°(B=3.992,P=0.004,95％CI:1.283～6.702),血管分形维数增大 0.090(B=0.090,P＜0.001,95％CI:0.078～0.102),血管平均管径减小 14.813 μm(B=-14.813,P＜0.001,95％CI:-16.474～-13.153).结论 成功构建视网膜血管分割模型.视网膜血管参数与性别、年龄、系统性疾病和眼部因素存在关联.
Abstract
Objective To analyze retinal vascular parameters and distribution characteristics in Chinese population via the fully automated quantitative measurement of retinal vascular morphological parameters based on artificial intelligence technology.Methods A cross-sectional study was performed.A total of 1 842 patients without fundus diseases who visited Beijing Tongren Hospital from January 2011 to December 2021 were included.Standardized questionnaires,blood draws and ophthalmologic examinations of enrolled subjects were conducted.Color fundus photographs centered on the optic disk of one eye of patients were collected,and a deep learning-based semantic segmentation network ResNet101-Unet was used to construct a vascular segmentation model for fully automated quantitative measurement of retinal vascular parameters.The main measurement indexes included retinal vascular branching angle,vascular fractal dimension,average vascular caliber,and average vascular tortuosity.To compare different retinal parameters between sexes,the correlation between the above parameters and ocular factors such as best corrected visual acuity,intraocular pressure,and axial length,as well as systemic factors such as sex,age,hypertension,diabetes mellitus,and cardiovascular disease was analyzed.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Beijing Tongren Hospital,Capital Medical University(No.20001220).Written informed consent was obtained from each subject.Results The model established in this study achieved an accuracy over 0.95 for both vascular and optic disk segmentation.The vascular branching angle,vascular fractal dimension,average vascular caliber,and average vascular tortuosity were(51.023± 11.623)°,1.573(1.542,1.592),64.124(60.814,69.053)μm,(0.001 062±0.000 165)°,respectively.Compared with females,males had larger vascular branching angle,smaller average vascular caliber and smaller vascular tortuosity,and the differences were statistically significant(all at P＜0.05).The average vascular caliber increased by 1.142 μm in people with cardiovascular disease compared to people without cardiovascular disease(B=1.142,P=0.029,95％CI:0.116-2.167).The average vascular tortuosity was positively correlated with hypertension(B=3.053x 10-5,P=0.002,95％CI:1.167×10-5-4.934×10-5)and alcohol consumption(B=1.036×10-5,P=0.014,95％CI:0.211×10-5-1.860×10-5)and negatively correlated with hyperlipidemia(B=-2.422×10-5,P=0.015,95％CI:-4.382×10-5-0.462×10-5).For each 1-mm increase in axial length,there was a decrease of 0.004 in vessel fractal dimension(B=-0.004,P＜0.001,95％CI:-0.006--0.002),a decrease of 0.266 μm in the average vessel caliber(B=-0.266,P=0.037,95％CI:-0.516--0.016),and a decrease of-2.45×10-5 ° in the average vessel tortuosity(B=-2.45×10-5,P＜0.001,95％CI:-0.313×10-5--0.177×10-5).For each 1.0 increase in BCVA,there was an increase of 3.992° in the vascular branch angle(B=3.992,P=0.004,95％CI:1.283-6.702),an increase of 0.090 in vascular fractal dimension(B=0.090,P＜0.001,95％CI:0.078-0.102)and a decrease of 14.813 μm in the average vascular diameter(B=-14.813,P＜0.001,95％CI:-16.474--13.153).Conclusions A model for retinal vascular segmentation is successfully constructed.Retinal vessel parameters are associated with sex,age,systemic diseases,and ocular factors.
基金项目
国家自然科学基金(82141128)
首都卫生发展科研专项(2020-1-2052)
北京市科委科技计划(Z201100005520045)
北京市科委科技计划(Z181100001818003)
NETL
NSTL
维普
万方数据