RÉSUMÉ
Objective To observe the correlations of pontine biological indicators on fetal brain median sagittal MRI with gestational week.Methods Data of head MRI of 226 normal fetuses without obvious abnormalities of central nervous system(normal group)and 17 fetuses with abnormalities(abnormal group)at gestational age of 23 to 38 weeks were retrospectively analyzed.Pontine biological indicators based on median sagittal MRI were obtained,including pons anteroposterior diameter(PAD),total pons area(TPA),pontine basal anteroposterior length(AP),pontine basal cranio-caudal length(CC),basis pontis area(BPA)and pontine angle of midbrain(MAP).According to the gestational week,the fetuses of normal group were divided into 8 subgroups.The distributing ranges of pontine biological indicators at different gestational weeks were analyzed,and the correlations of pontine biological indicators with gestational week in normal group were explored,and the developmental status of fetal pons in abnormal group were assessed.Results In normal group,PAD,TPA,AP,CC and BPA all showed linear positive correlation(r=0.887,0.914,0.787,0.866,0.865,all P＜0.001),while MAP was not significantly correlated with gestational week(P＞0.05).Among 17 fetuses in abnormal group,abnormal PAD or TPA was found each in 8 fetuses,abnormal AP was observed in 14,abnormal CC was noticed in 3 and abnormal BPA was found in 11 fetuses.Conclusion Fetal pontine biological indicators such as PAD,TPA,AP,CC and BPA on median sagittal MRI were positively correlated with gestational week,hence being able to be used for evaluating fetal pontine development.
RÉSUMÉ
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.