  Ultrasonography is an essential tool in mid-pregnancy for assessing fetal
development, appreciated for its non-invasive and real-time imaging
capabilities. Yet, the interpretation of ultrasound images is often complicated
by acoustic shadows, speckle noise, and other artifacts that obscure crucial
diagnostic details. To address these challenges, our study presents a novel
unsupervised anomaly detection framework specifically designed for fetal
ultrasound imaging. This framework incorporates gestational age filtering,
precise identification of fetal standard planes, and targeted segmentation of
brain regions to enhance diagnostic accuracy. Furthermore, we introduce the use
of denoising diffusion probabilistic models in this context, marking a
significant innovation in detecting previously unrecognized anomalies. We
rigorously evaluated the framework using various diffusion-based anomaly
detection methods, noise types, and noise levels. Notably, AutoDDPM emerged as
the most effective, achieving an area under the precision-recall curve of
79.8\% in detecting anomalies. This advancement holds promise for improving the
tools available for nuanced and effective prenatal diagnostics.
