anomalies and enabling timely implementation of effective interventions. Methods Singleton pregnant women who underwent 11- 13+6 week ultrasound examinations in the Department of Ultrasound Diagnosis of the First Medical Center, Chinese PLA General Hospital from January 2014 to December 2024 were included, cases with abnormal ultrasound findings were followed up for the results of amniocentesis genetic testing. According to the types of abnormalities, they were classified into structural abnormalities group, soft markers abnormalities group, and combined structural and soft markers abnormalities group. The incidence rates of genetic abnormalities were compared among the groups to analyze the correlation between ultrasound phenotypes and genetic abnormalities. LASSO-Logistic regression analysis was employed to screen relevant variables and construct a nomogram risk prediction model, aiming to evaluate the predictive value of different ultrasound phenotypes at 11-13+6 weeks of early pregnancy for genetic abnormalities. Model performance was assessed using AUC, ROC, DCA. Results A total of 360 singleton pregnant women with abnormal ultrasound findings at 11-13+6 weeks of early pregnancy were included in this study, with a mean age of (32.39±4.31) years. Among them, 89 cases (24.7%) had genetic abnormalities, predominantly chromosomal numerical abnormalities accounting for 82.0% (73/89), with trisomy 21 being the most common at 44.9% (40/89). The incidence of genetic abnormalities was 47.8% (11/ 23) in the structural abnormalities group, 18.9% (56/296) in the soft marker abnormalities group, and 53.7% (22/41) in the combined structural and soft marker abnormalities group, with significant difference (P < 0.05). Subgroup analysis revealed that the proportions of genetic abnormalities were significantly higher in groups with two structural abnormalities, two or more soft marker abnormalities, one structural abnormality combined with one soft marker abnormality, or multiple structural abnormalities combined with soft markers compared to the single soft marker abnormality group (P < 0.05). Among isolated soft marker abnormalities, the genetic abnormality rate was 17.4% in the NT≥3.0 mm group, higher than 13.9% in the 2.5 mm≤NT＜3.0 mm group, while the nasal bone abnormality group showed a rate of 25.0%. Notably, the risk further increased with the number of soft marker abnormalities, reaching up to 60.0% in the group with two or more abnormalities. LASSO-Logistic regression analysis identified nine key variables: maternal age, ultrasound gestational week, isolated soft marker abnormalities, NT classification, nasal bone abnormality, isolated structural abnormality, skin edema, cardiac structural abnormality, and cervical lymphatic hydrops. The constructed risk prediction model exhibited an area under the receiver operating characteristic curve (AUC) of 0.782 (95% CI: 0.723 - 0.841). The calibration curve indicated good consistency between predicted and actual probabilities, while the decision curve analysis (DCA) showed high net benefit across a threshold probability range of 0-0.9, supporting the model's clinical predictive value. Conclusion Ultrasonographic phenotypes with abnormalities at 11-13+6 weeks of early pregnancy are of significant predictive value in screening for fetal genetic abnormalities. The combination of multiple structural abnormalities with soft marker abnormalities indicates a highrisk profile, for which invasive prenatal diagnosis is recommended. Incorporating advanced testing techniques such as chromosomal microarray analysis (CMA) and whole exome sequencing (WES) can enhance diagnostic efficiency. The nomogram risk model constructed via LASSO-Logistic regression analysis effectively predicts fetal genetic abnormality risk in the early stage, assisting clinicians in streamlining clinical decision-making processes through a visual tool.