Craniofacial anomalies indicate early developmental disturbances and are usually linked to many genetic syndromes. Early diagnosis is critical, yet ultrasound (US) examinations often fail to identify these features. This study presents an AI-driven tool to assist clinicians in standardizing fetal facial axes/planes in 3D US, reducing sonographer workload and facilitating the facial evaluation. Our network, structured into three blocks-feature extractor, rotation and translation regression, and spatial transformer-processes three orthogonal 2D slices to estimate the necessary transformations for standardizing the facial planes in the 3D US. These transformations are applied to the original 3D US using a differentiable module (the spatial transformer block), yielding a standardized 3D US and the corresponding 2D facial standard planes. The dataset used consists of 1180 fetal facial 3D US images acquired between weeks 20 and 35 of gestation. Results show that our network considerably reduces inter-observer rotation variability in the test set, with a mean geodesic angle difference of 14.12$^{\circ}$ $\pm$ 18.27$^{\circ}$ and an Euclidean angle error of 7.45$^{\circ}$ $\pm$ 14.88$^{\circ}$. These findings demonstrate the network's ability to effectively standardize facial axes, crucial for consistent fetal facial assessments. In conclusion, the proposed network demonstrates potential for improving the consistency and accuracy of fetal facial assessments in clinical settings, facilitating early evaluation of craniofacial anomalies.

本研究解决了超声检查中常常无法识别颅面畸形的问题，提出了一种基于人工智能的工具，能够自动标准化胎儿面部轴线，减轻超声技师的工作负担并提高面部评估的准确性。研究结果表明，该工具显著降低了观察者间的旋转变异性，提升了胎儿面部评估的一致性和准确性，具有重要的临床应用潜力。

3D胎儿超声图像的面部轴线自动标准化