ArticleJournal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine2026
Feasibility and Reproducibility of a Structure-Guided Deep Learning Model for Automatic Detection of the Standard Sagittal Plane in First-Trimester Nuchal Translucency Assessment Using 3D Ultrasound.
Article in Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivesAccurate nuchal translucency (NT) measurement for assessing the risk of fetal genetic abnormalities requires precise acquisition of the mid-sagittal plane (MSP). However, achieving an appropriate MSP is technically challenging due to anatomical variability and operator dependence inherent in conventional 2-dimensional (2D) ultrasound. This study aimed to develop and validate a novel deep learning algorithm for automated fetal MSP extraction from 3-dimensional (3D) ultrasound volumes utilizing intracranial structure segmentation to overcome the limitations of conventional methods reliant on facial landmarks.
methodsIn this prospective study, we developed and evaluated "3D MSP-net," a convolutional neural network (CNN)-based model for automated MSP extraction, involving singleton pregnant women undergoing first-trimester NT screening. Using achieved 3D volume data, 3D MSP-net was validated against the conventional 2D manual method and a commercially available rule-based automated system (5D NT™). Two maternal-fetal medicine (MFM) specialists independently assessed the resulting MPSs to determine the performance for demonstrating the feasibility and high reproducibility of the 3D MSP-net.
results3D MSP-net achieved an MSP extraction success rate of 91.6%, comparable to that of the conventional 2D manual method and significantly superior to the rule-based 3D algorithm. NT measurements were comparable between the conventional 2D manual approach and MSPs derived from 3D MSP-net (1.4 ± 0.5 mm versus 1.4 ± 0.4 mm; p = .444). These results were reproducible on external validation. Moreover, the 3D MSP-net maintained robust performance even under challenging conditions, such as increased maternal body mass index and different scan deviation angles.
conclusionThe 3D MSP-net, our artificial intelligence (AI) model that utilizes intracranial landmarks for MSP reconstruction, enables improved efficiency, standardization, and reliability for first-trimester fetal screening addressing a key challenge in prenatal diagnostics.
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