结构引导深度学习模型在早孕期NT评估中自动检测标准矢状面的可行性与可重复性
本研究开发并验证了基于颅内结构分割的3D MSP-net深度学习模型，用于从3D超声容积中自动提取胎儿正中矢状面。该模型成功率达91.6%，与常规2D手动方法相当且显著优于基于规则的3D算法，NT测量值在两种方法间无显著差异（1.4±0.5 mm vs 1.4±0.4 mm，p=.444），外部验证可重复，且在母亲BMI增高和扫描偏差角度等挑战性条件下保持稳健性能。
要点
- 3D MSP-net利用颅内结构标志自动提取正中矢状面，成功率达91.6%，与手动2D方法相当。
- 自动提取的MSP用于NT测量与常规2D手动测量结果一致（1.4±0.5 mm vs 1.4±0.4 mm，p=.444）。
- 模型在母亲BMI增高及扫描偏差角度下仍保持稳健，外部验证可重复。
核心结论
- 3D MSP-net利用颅内标志重建MSP，可提高早孕期胎儿筛查的效率、标准化和可靠性。
研究设计与样本
- 前瞻性研究开发并评估基于卷积神经网络的3D MSP-net模型，纳入接受早孕期NT筛查的单胎孕妇。
- 使用获取的3D超声容积数据，将3D MSP-net与常规2D手动方法及商用规则式自动系统（5D NT™）进行验证比较。
- 由两名母胎医学专家独立评估所得MSP，以确定3D MSP-net的可行性和高可重复性。
诊断标准与关键数值
- 3D MSP-net的MSP提取成功率为91.6%，与常规2D手动方法相当，显著优于基于规则的3D算法。
- 常规2D手动方法与3D MSP-net所得MSP的NT测量值比较：1.4±0.5 mm vs 1.4±0.4 mm，p=.444。
- 上述结果在外部验证中可重复。
临床应用与阅片要点
- 可利用3D MSP-net从3D超声容积自动提取标准正中矢状面，辅助早孕期NT测量的标准化。
- 在母亲BMI增高或扫描偏差角度等挑战性条件下，该模型仍可保持稳健性能。
以上解读依据论文摘要（全文未开放获取或许可不允许翻译转载）。
英文原文（摘要）· 点击展开
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.
引用
- Kwon H, Hur H, Cho HC, et al. 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. J Ultrasound Med. 2026. PMID: 42104541. doi:10.1002/jum.70281
供执业医师工作参考，不替代临床判断；数值与分级以指南原文为准。