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胎儿心脏超声智能导航技术联合虚拟智能超声辅助技术在胎儿右心室双出口筛查中的应用价值
引用本文:李玉惠,赵博文,周超瑜,赵蓓,黄超,庞海苏,狄敏.胎儿心脏超声智能导航技术联合虚拟智能超声辅助技术在胎儿右心室双出口筛查中的应用价值[J].中华医学超声杂志,2022,19(2):142-149.
作者姓名:李玉惠  赵博文  周超瑜  赵蓓  黄超  庞海苏  狄敏
作者单位:1. 310016 杭州,浙江大学医学院附属邵逸夫医院超声科;310006 杭州,浙江省胎儿心脏超声诊断技术指导中心 浙江大学邵逸夫临床医学研究所;310006 杭州,浙江大学医学院附属妇产科医院超声科2. 310006 杭州,浙江省胎儿心脏超声诊断技术指导中心 浙江大学邵逸夫临床医学研究所;310006 杭州,浙江大学医学院附属妇产科医院超声科
基金项目:浙江省基础公益研究计划项目(LGF18H180004)
摘    要:目的评价胎儿心脏超声智能导航(FINE)联合虚拟智能超声辅助(VIS-Assistance?)技术在胎儿右心室双出口(DORV)产前筛查中的应用价值。 方法纳入2018年9月至2019年12月在浙江大学医学院附属邵逸夫医院超声科行胎儿心脏超声检查且诊断为胎儿DORV的孕妇30例。以超声心动图四腔心切面为初始切面,采集3~5个心脏三维容积数据并选取其中图像质量最佳者,运用FINE技术进行脱机分析,并利用VIS-Assistance?功能对诊断平面图像进行优化处理,获取9个标准诊断切面,分别对4个诊断切面(三血管气管切面、四腔心切面、左心室流出道切面、右心室流出道切面)的要素化信息进行评分,分析各诊断切面及其诊断要素的显示率,分别对同一位观察者在不同时间及不同观察者对相同诊断切面的评分结果进行比较。 结果对30例胎儿的容积数据进行FINE及VIS-Assistance?处理,其中25例胎儿容积数据能够用于分析,获取成功率为83.3%。4个诊断切面的显示率分别为84.0%、88.0%、72.0%、84.0%。比较同一位观察者在不同时间的评分结果、相对缺乏经验的2位医师的评分结果之间进行比较以及2位医师分别与经验丰富的医师比较,其差异均无统计学意义(P均>0.05)。 结论在DORV病例中,FINE与VIS-Assistance?相结合,生成4个特异的心脏诊断切面成功率较高,并且具有较好的操作一致性及重复性,可用于胎儿DORV的筛查。

关 键 词:先天性心脏病  胎儿  超声心动图  右心室双出口  人工智能  
收稿时间:2020-07-09

Clinical value of Fetal Intelligent Navigation Echocardiography and Virtual Intelligent Sonographer Assistance in screening of fetal double-outlet right ventricle
Yuhui Li,Bowen Zhao,Chaoyu Zhou,Bei Zhao,chao Huang,Haisu Pang,Min Di.Clinical value of Fetal Intelligent Navigation Echocardiography and Virtual Intelligent Sonographer Assistance in screening of fetal double-outlet right ventricle[J].Chinese Journal of Medical Ultrasound,2022,19(2):142-149.
Authors:Yuhui Li  Bowen Zhao  Chaoyu Zhou  Bei Zhao  chao Huang  Haisu Pang  Min Di
Abstract:ObjectiveTo evaluate the value of Fetal Intelligent Navigation Echocardiography (FINE) and Virtual Intelligent Sonographer Assistance (VIS-Assistance?) in the screening of fetal double-outlet right ventricle (DORV). MethodsThis study included 30 pregnant women diagnosed with fetal DORV who underwent fetal echocardiography at the Sir Run Run Shaw Hospital from September 2018 to December 2019. Three to five sets of three-dimensional volume data of the heart were collected and the best image quality was selected using the FINE technique for off-line analysis. The diagnostic plane image was optimized by using VIS-Assistance? function, and nine standard diagnostic planes were obtained. The essential information of four diagnostic sections (three-vessel tracheal view, four chamber view, left ventricular outflow view , and right ventricle outflow view) was scored, the display rates of each diagnostic plane and its diagnostic factors were analyzed, and the scoring results obtained by the same observer in different periods and those obtained by different observers in the same period were compared. ResultsFINE and VIS-Assistance? were applied to the fetal volume data in 30 cases, of which 25 were analyzed, with a success rate of 83.3%. The display rates of the four diagnostic sections were 84.0%, 88.0%, 72.0%,and 84.0%, respectively. There was no significant difference in the scores obtained by the same observer at different time points, the scores obtained by two relatively inexperienced physicians, and the scores obtained by two relatively inexperienced physicians and one experienced physician (P>0.05). ConclusionFINE combined with VIS-Assistance? demonstrates a high success rate in producing four specific cardiac diagnostic sections in fetuses with DORV, has good consistency and repeatability, and therefore can be used for prenatal screening of fetal DORV.
Keywords:Congenital heart defect  Fetus  Echocardiography  Double outlet right ventricular  Artificial intelligence  
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