Research and application of artificial intelligence quality control model of fetal heart in the first trimester
10.3760/cma.j.cn131148-20230522-00281
- VernacularTitle:妊娠早期胎儿心脏人工智能质控模型的研究与应用
- Author:
Qiaozhen ZHU
1
;
Ying TAN
;
Meifang ZHANG
;
Xin WEN
;
Yao JIANG
;
Yue QIN
;
Ying YUAN
;
Hongbo GUO
;
Guiyan PENG
;
Wenlan HUANG
;
Lingxiu HOU
;
Shengli LI
Author Information
1. 南方医科大学第一临床医学院，广州 510515
- Keywords:
Ultrasonography;
First trimester;
Fetal heart;
Quality control;
Artificial intelligence
- From:
Chinese Journal of Ultrasonography
2023;32(11):952-958
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop an artificial intelligence (AI) quality control model of fetal heart in the first trimester and verify its effectiveness.Methods:A total of 18 694 images of the four-chamber view(4CV) and three-vessel and tracheal view(3VT) of fetal heart in the first trimester were selected from Shenzhen Maternal and Child Health Hospital Affiliated to Southern Medical University since January 2022 to December 2022. A total of 14 432 images were manually annotated. The one-stage target detection algorithm YOLO V5 was used to train the AI quality control model in the first trimester of fetal heart, and 4 262 images (golden standard set by expert group) were used to evaluate the application effectiveness of AI quality control model. Kappa consistency test was used to compare the results of section classification and standard degree judgment from AI quality control model, Doctor 1(D1) and Doctor 2(D2).Results:①Precision of the AI quality control model was 0.895, recall was 0.852, mean average precision (mAP 50) was 0.873.The average precision(AP) of the AI quality control model for section classification was 0.907 (4CV) and 0.989 (3VT), respectively. ②Compared with the gold standard, the overall coincidence rate and consistency of section classification of AI quality control model, D1 and D2 were 99.91% (Kappa=0.998), 100% (Kappa=1.000), 100% (Kappa=1.000), respectively. The coincidence rate and consistency of the plane standard degree evaluation from the AI quality control model, D1 and D2 were 97.46% (Weighted Kappa=0.932), 93.73% (Weighted Kappa=0.847), and 93.12% (Weighted Kappa=0.832), respectively. Strong consistency was displayed. Moreover, AI quality control model showed the highest coincidence rate and the strongest consistency in judging section standard degree, which was superior to manual quality control. The time-consuming of AI quality control (0.012 s/sheet) was significantly less than the way of manual quality control (4.76-6.11 s/sheet)( Z=-8.079, P<0.001). Conclusions:The use of artificial intelligent fetal heart quality control model in the first trimester can effectively and accurately control the image quality.
