首页分享基于人工智能模型量化视网膜血管特征参数预测子痫前期的可行性研究

基于人工智能模型量化视网膜血管特征参数预测子痫前期的可行性研究

来源：花匠小妙招时间：2025-08-06 11:11

摘要：

目的·基于人工智能（artificial intelligence，AI）模型，探讨视网膜血管特征参数在子痫前期（preeclampsia，PE）中的预测能力。方法·回顾性纳入2020年6月—2021年1月在妊娠16周前于同济大学附属妇产科医院建卡、规律产检、行眼底图像拍摄并于该院分娩的789例单胎活产孕妇。根据孕妇是否发生妊娠期高血压疾病（hypertensive disorders of pregnancy，HDP），将其分为正常妊娠组（n=685）和HDP组（n=104）；根据是否发生PE，将HDP组分为妊娠期高血压（gestational hypertension，GH）组（n=36）和PE组（n=68）；且根据发病孕周，将PE组分为早发型PE组（发病孕周<34周）和晚发型PE组（发病孕周≥34周）。获取入组孕妇的眼底图像，利用AI算法诊断眼底病变特征、量化视网膜血管特征参数并对眼底特征、视网膜血管特征参数进行比较分析。采用单因素Logistic回归模型分析PE发生的影响因素，并采用多因素Logistic回归模型进一步评估视网膜血管特征参数等与PE发生的相关性。采用受试者操作特征曲线（receiver operator characteristic curve，ROC curve，ROC曲线）的曲线下面积（area under the curve，AUC）分析视网膜血管特征参数等对PE（早发型PE和晚发型PE）的预测能力。结果·眼底特征及视网膜血管特征参数的分析结果显示，正常妊娠组和PE组孕妇的视网膜中央动脉直径等效值（central retinal artery equivalent，CRAE）、视网膜中央静脉直径等效值（central retinal vein equivalent，CRVE）、视网膜动静脉比值（arteriole-to-venular ratio，AVR）、视网膜动脉弯曲度和视网膜动脉分形维数间差异具有统计学意义（均P<0.05）。单因素Logistic回归分析显示，孕中期平均动脉压（mean arterial pressure，MAP）、孕中期胎儿估计体质量（estimated fetal weight，EFW）、CRAE、CRVE、AVR、视网膜动脉弯曲度和视网膜动脉分形维数是PE发生的影响因素（均P<0.05）。多因素Logistic回归分析显示，孕中期EFW、CRAE、CRVE、AVR、视网膜动脉弯曲度和视网膜动脉分形维数是PE发生的保护因素，孕中期MAP是其危险因素（均P<0.05）。ROC曲线的分析结果显示，母体危险因素+孕中期产检资料（包括MAP和EFW）+视网膜血管特征参数模型对PE预测能力较好［AUC（95% CI）=0.784（0.725?0.843）］，且其对早发型PE的预测能力更优［AUC（95% CI）=0.840（0.756?0.924）］。结论·使用基于AI模型量化的视网膜血管特征参数联合母体危险因素、孕中期产检资料（包括MAP和EFW）能够较好地预测PE（特别是早发型PE）的发生。

关键词: 子痫前期, 视网膜血管, 人工智能

Abstract:

Objective ·To explore the predictive capability of retinal vascular features in preeclampsia (PE) based on artificial intelligence (AI) models. Methods ·This retrospective study enrolled 789 pregnant women who registered from June 2020 to January 2021 at Shanghai First Maternity and Infant Hospital of Tongji University in the first 16 weeks of gestation. These women underwent regular prenatal examinations, had retinal fundus images captured, and delivered singleton live births at the hospital. According to whether they developed hypertensive disorders of pregnancy (HDP), they were divided into unaffected group (n=685) and HDP group (n=104). Within the HDP group, pregnancies were further categorized into gestational hypertension (GH) group (n=36) and PE group (n=68) based on the occurrence of PE. Based on the gestational age at onset, the PE group was further divided into early-onset PE group (gestational age<34 weeks) and late-onset PE group (gestational age≥34 weeks). Fundus images of the pregnant women were obtained, and an AI algorithm was utilized to diagnose retinal lesions and quantify retinal vascular features. Comparative analyses were conducted on fundus features and retinal vascular features. Univariate Logistic regression model was employed to analyze the influencing factors of PE occurrence, and multivariate Logistic regression model was further utilized to assess the correlation between retinal vascular features and the occurrence of PE. The predictive capability of retinal vascular features for PE (both early- and late-onset PE) was analyzed by using area under the curve (AUC) of receiver operator characteristic curve (ROC curve). Results ·The comparative analysis of fundus features and retinal vascular features demonstrated statistically significant differences between the unaffected group and PE group in terms of central retinal artery equivalent (CRAE), central retinal vein equivalent (CRVE), arteriole-to-venular ratio (AVR), retinal artery tortuosity and retinal artery fractal dimension (all P<0.05). Univariate Logistic regression analysis indicated that second-trimester mean arterial pressure (MAP), second-trimester estimated fetal weight (EFW), CRAE, CRVE, AVR, retinal artery tortuosity and retinal artery fractal dimension were the influencing factors for PE occurrence (all P<0.05). Multivariate Logistic regression analysis revealed that second-trimester EFW, CRAE, CRVE, AVR, retinal artery tortuosity and retinal artery fractal dimension were the protective factors for the occurrence of PE, while second-trimester MAP was the risk factor for PE (all P<0.05). The analysis of ROC curves revealed that maternal risk factors along with second-trimester prenatal examination data (including MAP and EFW) and retinal vascular features model had good predictive ability for PE [AUC (95% CI)=0.784 (0.725－0.843), and this model exhibited better predictive capability for early-onset PE, with an AUC (95% CI) of 0.840 (0.756－0.924). Conclusion ·The integration of quantified retinal vascular features based on AI models with maternal risk factors and second-trimester prenatal examination data (including MAP and EFW) enables a more effective prediction of PE occurrence, particularly early-onset PE.

Key words: preeclampsia (PE), retinal vessel, artificial intelligence (AI)

中图分类号:

R714.7