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Objective:To explore the effectiveness of artificial intelligent (AI) quality control system on the standard mid-sagittal view of fetal face in 11-13 +6 weeks of gestation. Methods:The quality of the images was evaluated using online " Intelligent ultrasonic quality control system" for 1 063 sections of fetal Nuchal translucency ultrasound images in the Ultrasound Department of Shenzhen Maternity and Child Healthcare Hospital of Southern Medical University. The manual quality control results and time consuming of two experts were compared with AI. The gold standard was decided by experts group with higher seniority.Results:The overall standard rate, substandard rate and nonstandard rate of the image evaluated by the intelligent quality control system was 87.3%, 2.82%, and 9.88%. The overall accuracy of images was 96.6%. The coincidence rates between AI and the two experts were 96.1% and 96.3%, respectively, with strong consistency (Kappa were 0.835 and 0.845 respectively). The time required for intelligent quality control was significantly shorter than manual quality control (208 s vs 6 696 s/6 602 s). All the differences were statistically significant ( Z=-3.981, P<0.001). Conclusions:The intelligent quality control system could accurately and quickly evaluate whether the mid-sagittal view of fetal face in 11-13 +6 weeks of gestation is standard.
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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.