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 <scp>3D</scp> Ultrasound

OBJECTIVES: Accurate 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. METHODS: In 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. RESULTS: 3D 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. CONCLUSION: The 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.