Intelligent quality assessment of ultrasound images for fetal nuchal translucency measurement during the first trimester of pregnancy based on deep learning models.
Authors
Affiliations (5)
Affiliations (5)
- Department of Ultrasound Medicine, Medical School, South China Hospital, Shenzhen University, Shenzhen, P. R. China.
- Medical Research Department, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, P. R. China.
- Department of Ultrasound Medicine, Medical School, South China Hospital, Shenzhen University, Shenzhen, P. R. China. [email protected].
- Department of Ultrasound, Medical School, Shenzhen University General Hospital, Shenzhen University, Shenzhen, P. R. China. [email protected].
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen University Medical School, Shenzhen University, Shenzhen, P. R. China. [email protected].
Abstract
As increased nuchal translucency (NT) thickness is notably associated with fetal chromosomal abnormalities, structural defects, and genetic syndromes, accurate measurement of NT thickness is crucial for the screening of fetal abnormalities during the first trimester. We aimed to develop a model for quality assessment of ultrasound images for precise measurement of fetal NT thickness. We collected 2140 ultrasound images of midsagittal sections of the fetal face between 11 and 14 weeks of gestation. Several image segmentation models were trained, and the one exhibiting the highest DSC and HD 95 was chosen to automatically segment the ROI. The radiomics features and deep transfer learning (DTL) features were extracted and selected to construct radiomics and DTL models. Feature screening was conducted using the <i>t</i>-test, Mann-Whitney <i>U</i>-test, Spearman’s rank correlation analysis, and LASSO. We also developed early fusion and late fusion models to integrate the advantages of radiomics and DTL models. The optimal model was compared with junior radiologists. We used SHapley Additive exPlanations (SHAP) to investigate the model’s interpretability. The DeepLabV3 ResNet achieved the best segmentation performance (DSC: 98.07 ± 0.02%, HD 95: 0.75 ± 0.15 mm). The feature fusion model demonstrated the optimal performance (AUC: 0.978, 95% CI: 0.965–0.990, accuracy: 93.2%, sensitivity: 93.1%, specificity: 93.4%, PPV: 93.5%, NPV: 93.0%, precision: 93.5%). This model exhibited more reliable performance compared to junior radiologists and significantly improved the capabilities of junior radiologists. The SHAP summary plot showed DTL features were the most important features for feature fusion model. The proposed models innovatively bridge the gaps in previous studies, achieving intelligent quality assessment of ultrasound images for NT measurement and highly accurate automatic segmentation of ROIs. These models are potential tools to enhance quality control for fetal ultrasound examinations, streamline clinical workflows, and improve the professional skills of less-experienced radiologists. The online version contains supplementary material available at 10.1186/s12884-025-07863-y.