Fetal magnetic resonance imaging (MRI) is a intriguing tool to gain insights into early human development. Diffusion MRI is of particular interest to study neuronal development in vivo. However, fetal motion hampers accurate quantification. We suggest an automated quality check for a combined multi-echo diffusion-weighted fetal sequence on the low field (0.55T) consisting of deep learning-based masking of the brain and quality assessment. Results from 56 fetal datasets between 17 and 41 weeks gestational age illustrate the ability to obtain high-quality masks and transparent, insightful quality scores. Next, the achieved automatic assessment will be performed in real-time to guide the scan, initiate possible field-of-view (FOV) shifts, or repeat individual volumes.