ArticleBMC pregnancy and childbirth2025
Intelligent quality assessment of ultrasound images for fetal nuchal translucency measurement during the first trimester of pregnancy based on deep learning models.
Article in BMC pregnancy and childbirth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Artificial intelligence-assisted quality assessment of mid-trimester ultrasound examinations using large vision-language models.BMC pregnancy and childbirth · 2026Article
- A Review and Perspective of Techniques for Autonomous Robotic Ultrasound Acquisitions.Sensors (Basel, Switzerland) · 2026Review
- AI-based quality control was associated with improved fetal ultrasound image quality in low-resource settings: a real-world multicenter study from West China.BMC medicine · 2026Observational
- Radiomics analysis of early pregnancy ultrasound images to predict viability at the end of first trimester.Scientific reports · 2026Article
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8 authors.
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Abstract
objectiveAs 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.
methodWe 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 t-test, Mann-Whitney U-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.
resultsThe 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.
conclusionThe 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.
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