摘要
目的运用混合人工智能(AI)模型比较贫血胎儿与正常胎儿的心脏大小、形态以及功能, 探讨AI在定量评估贫血胎儿心功能中的应用价值。方法回顾性收集2018-2024年中山大学附属第七医院经脐静脉穿刺确诊的15例贫血胎儿(贫血组)与32例正常胎儿(正常组)为研究对象。纳入胎儿四腔心超声视频及左右心室节段的数量:贫血组分别为44个、1 056节段, 正常组分别为46个、1 104节段。基于胎儿动态四腔心图像, 利用混合AI模型获取心脏测量相关参数, 包括:四腔心的舒张末期长径(BAL)、横径(TW)、整体球形指数(GSI)、舒张末期面积(EDA), 24节段左、右心室舒张末期横径(LVEDD、RVEDD), 24节段左、右心室球形指数(LVSI、RVSI), 左、右心室整体纵向应变(LVGLS、RVGLS), 左、右心室面积变化率(LVFAC、RVFAC), 24节段左、右心室短轴缩短率(LVFS、RVFS)等参数及其对应的Z评分。比较两组胎儿心脏大小、形态及功能参数的差异。对正常组参数(BAL、TW、EDA、GSI、LVGLS、RVGLS、LVFAC及RVFAC)与孕周进行Pearson相关性分析。评估AI技术与fetal HQ技术在正常组和贫血组中的测量一致性。结果贫血组与正常组BAL、TW、EDA和GSI差异无统计学意义(均P>0.05)。贫血组第3~24节段的RVEDD大于正常组(均P<0.05), 且LVEDD、RVEDD 24个节段Z评分总异常率显著高于正常组(均P<0.001)。贫血组第7~10、12、14~15节段LVSI以及第1~23节段RVSI低于正常组(均P<0.05), 且LVSI、RVSI 24个节段Z评分总异常率显著高于正常组(均P<0.001)。贫血组LVGLS、LVFAC低于正常组(均P<0.05), 两组间RVGLS、RVFAC差异无统计学意义(均P>0.05)。贫血组第2、5~8、11~13节段LVFS低于正常组(均P<0.05)。正常组胎儿BAL、TW、EDA与孕周呈显著正相关(r=0.913、0.947、0.907, 均P<0.001), 而GSI、LVGLS、RVGLS、LVFAC和RVFAC与孕周无相关性或呈弱相关性(r=-0.221、0.353、0.515、-0.409、-0.425)。正常组和贫血组AI与传统方法fetal HQ技术评估参数的组内相关系数(ICC)分别为0.788、0.837, 一致性良好。结论 AI可简便地定量评估胎儿心脏的大小、形态和收缩功能。胎儿贫血主要影响右心室形态及左心室整体收缩功能, 表现为右心室球形重构及LVGLS、LVFAC及节段性LVFS异常。混合AI模型在胎儿心功能评估中具有潜在的应用价值。
Objective:To compare the cardiac size,morphology,and function between anemic and normal fetuses using a hybrid artificial intelligence(AI)model,and to evaluate the utility of AI in quantitatively assessing fetal cardiac function in cases of anemia.Methods:A retrospective study was conducted by collecting data from 2018 to 2024 at the Seventh Affiliated Hospital of Sun Yat-sen University,including 15 cases of anemic fetuses(anemia group)diagnosed through umbilical venous puncture and 32 cases of normal fetuses(control group).Four-chamber fetal cardiac ultrasound videos and left/right ventricular segments were included,with 44 videos and 1056 segments in the anemia group,and 46 videos and 1104 segments in the control group.Based on dynamic four-chamber heart images,the hybrid AI model was employed to extract heart measurement parameters,including basal-apical length(BAL),transverse width(TW),global sphericity index(GSI),end-diastolic area(EDA),24-segment left and right ventricular end-diastolic diameter(LVEDD,RVEDD),segmental sphericity index(LVSI,RVSI),global longitudinal strain(LVGLS,RVGLS),fractional area change(LVFAC,RVFAC),segmental fractional shortening(LVFS,RVFS),along with their corresponding Z-scores.The differences in cardiac size,morphology,and function parameters between the two groups were compared.Pearson correlation analysis was performed for the parameters of the control group(BAL,TW,EDA,GLS,LVGLS,RVGLS,LVFAC,and RVFAC)against gestational age.The measurement consistencies of AI technology and fetal HQ technology in normal and anemia groups were evaluated.Results:No significant differences were found in BAL,TW,EDA,or GSI between groups(all P>0.05).RVEDD in segments 3-24 was significantly larger in the anemia group(all P<0.05),with significantly higher Z-score abnormality rates for LVEDD and RVEDD across 24 segments(both P<0.001).LVSI in segments 7-10,12,14-15 and RVSI in segments 1-23 were lower in the anemia group(all P<0.05),with significantly higher Z-score abnormality rates for LVSI and RVSI across 24 segments(both P<0.001).The absolute values of LVGLS and LVFAC were significantly reduced in the anemia group(both P<0.05),while the absolute values of RVGLS and RVFAC showed no significant differences(both P>0.05).Segmental LVFS values were significantly lower in the anemia group for segments 2,5-8,11-13(all P<0.05).In the control group,BAL,TW,and EDA positively correlated with gestational age(r=0.913,0.947,0.907;all P<0.001),while GSI,LVGLS,RVGLS,LVFAC and RVFAC showed no or weak correlations(r=-0.221,0.353,0.515,-0.409,-0.425).The intraclass correlation coefficient(ICC)between AI-based and conventional fetal HQ evaluations were 0.788 for the control group and 0.837 for the anemia group,indicating good consistency.Conclusions:AI offers a reliable approach for quantitatively evaluating fetal cardiac size,shape,and systolic function.Fetal anemia primarily affects right ventricular morphology and left ventricular systolic performance,characterized by spherical remodeling of the right ventricle and reductions in LVGLS,LVFAC,and segmental LVFS.The hybrid AI model holds potential value in fetal cardiac function assessment.
作者
黄羽君
朱云晓
袁鲲
汪南
朱晓敏
李清莹
王康婷
方群
Huang Yujun;Zhu Yunriao;Yuan Kun;Wang Nan;Zhu Xiaomin;Li Qingying;Wang Kangting;Fang Qun(Department of Medical Ultrasonics,the Seventh Affiliated Hospital of Sun Yat-sen University,Shenzhen 518107,China;Guangzhou Aiyunji Information Technology Co.,Ltd,Guangdong 510030,China;Prenatal Diagnosis Center,the Seventh Affiliated Hospital of Sun Yat-sen University,Shenzhen 518107,China)
出处
《中华超声影像学杂志》
北大核心
2025年第7期586-593,共8页
Chinese Journal of Ultrasonography
基金
2020年深圳市科创委基金面上项目(JCYJ20190814170205768)。
关键词
人工智能
胎儿贫血
超声心动描记术
心功能评估
分段定量分析
Artificial intelligence
Fetal anemia
Echocardiography
Fetal heart function
Segmented quantitative analysis
