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Objective To evaluate the relationship between first trimester (11 ~ 13+ 6 gestational weeks) ultrasonographic soft markers (USMs) and fetal adverse pregnancy outcomes. Methods A retrospective study, singleton pregnancies were recruited from the first-trimester scanning (FTS) during May 2017 to August 2021 at the Affiliated Suzhou Hospital of Nanjing Medical University. All fetuses were followed up till birth or induced abortion. The spectrum and detection rate of USMs during the first trimester were studied. The correlation between USMs and adverse pregnancy outcomes was analyzed to evaluate the management of fetus with 1st trimester USMs. Results Among 7355 fetuses who underwent FTS, 6868 fetuses with complete follow-up outcomes were recruited in the study. Total 1105 USMs were reported among 963 fetuses during the first trimester. According to the detection rate, the most frequently detected USMs were increased nuchal translucency (NT) thickeness (6.70%, 460/6868), followed by echogenic intracardiac focus (EIF) (5.10%, 350/6868), absent/reversed a-wave of ductus venosus (1.35%, 93/6868) and absence/hypoplasia of the nasal bone (NB) (1.03%, 71/6868). The incidence of adverse pregnancy outcomes in fetuses with two or more USMs was 36.44% (43/118), which was significantly higher (χ2 = 114.50, P P P P = 0.30; P = 0.77). Conclusions The ultrasonographic soft markers found during the first trimester, especially multiple USMs, or USMs are associated with structural malformations, are potential soft markers in predicting fetal adverse pregnancy outcomes.

Objectives Fetal growth restriction(FGR) is one of the most common causes of perinatal death and various short- and long-term complications. Accurate identification of fetal growth restriction is essential to reduce adverse perinatal outcomes. With the development of machine learning, many predictive models for fetal growth restriction have emerged. We assessed their performance by conducting systematic reviews and meta-analyses. Methods A systematic literature search was performed for relevant studies reported before May 31, 2022. The quality of the studies was assessed in strict accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (version 2020). Models with the reported area under the receiver operating characteristic (AUROC) indicator curve were meta-analyzed to determine the factors that contributed most to model performance. Results After the screening, 20 papers were eligible for synthesis, 19 were rated as high risk of bias, and 1 was rated as unclear risk of bias. From which 37 models were extracted, the c-statistic of the pooled random effects model (I2 = 96.0%) was 0.8123 (0.7824–0.8433, 95%CI). Among them, the c-statistic of the pooled risk model using the consensus developed by the Delphi procedure as the defined model was 0.81 (0.77–0.85, 95%CI), and the c-statistic of the pooled risk model using other defined models was 0.83 (0.78–0.87, 95%CI). In selecting predictors, most models were constructed by combining basic maternal characteristics, maternal pregnancy radionics, and serological indicators during pregnancy. Conclusion Machine learning has an excellent predictive value for FGR, which indicates that practical machine learning can be used as a possible means of FGR identification. However, improvements are needed in terms of quality and study design. We look forward to a unified definition and establishing an effective scoring tool to identify FGR accurately and intervene in time.