Application of artificial intelligence in screening the four-chamber view of fetal echocardiography
10.3760/cma.j.cn131148-20200304-00138
- VernacularTitle:人工智能技术在胎儿超声心动图四腔心切面筛查中的应用
- Author:
Xiaoxue ZHOU
1
;
Yingying ZHANG
;
Ye ZHANG
;
Jiancheng HAN
;
Xiaowei LIU
;
Xiaoyan GU
;
Lin SUN
;
Ying ZHAO
;
Yanping RUAN
;
Yihua HE
Author Information
1. 首都医科大学附属北京安贞医院超声科 胎儿心脏病母胎医学研究北京市重点实验室 心血管疾病精准医学北京实验室 100029
- From:
Chinese Journal of Ultrasonography
2020;29(8):668-672
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the value of artificial intelligence in screening normal or abnormal four-chamber view of the fetal heart.Methods:Selecting 3 996 pictures of normal and abnormal end systolic four chamber views and 450 video clips from the database of Beijing Key Laboratory of Fetal Heart Disease Maternal and Fetal Medicine Research in Beijing Anzhen Hospital as training set, test set and verification set to train, test and verify DGACNN model. ①Comparing DGACNN, DGACNN-ALOCC and other classification models(Densenet, Resnet50, InceptionV3, InceptionResnetV2) to detect the model with the most advanced level by recognizing 200 normal pictures and 200 abnormal pictures. ②Fetal echocardiographers were divided into three groups according to their experiences: primary, intermediate and advanced, 3 doctors in each group, and comparing the average score between each group or three groups and DGACNN by recognizing 100 normal pictures and 100 abnormal pictures.Results:①When the the false positive rate(FPR) was in the range of 20%, the recognition accuracy of DGACNN was the highest with 0.850, the recognition accuracy of other models were DGACNN-ALOCC 0.835, Densenet 0.780, Resnet50 0.700, InceptionV3 0.670, InceptionResnetV2 0.650, respectively. ②When FPR was in the range of 20%, the area under ROC curve of DGACNN was the largest with 0.881, the area under ROC curve of other models were DGACNN-ALOCC 0.864, Densenet 0.850, Resnet50 0.822, Inceptionv3 0.779, InceptionResnetV2 0.703, respectively. ③When the FPR was in the range of 20%, the average recognition accuracy of the senior fetal echocardiographer group was the highest with 0.863, followed by DGACNN 0.840, which was higher than the average recognition accuracy of the primary and intermediate groups with 0.760, 0.807; the average recognition accuracy of DGACNN was higher than the total average recognition accuracy of the primary, intermediate and advanced groups with 0.810.Conclusions:Artificial intelligence is accessible in screening four chamber view of fetal echocardiography, with high recognition accuracy.
