  Predicting fetal weight at birth is an important aspect of perinatal care,
particularly in the context of antenatal management, which includes the planned
timing and the mode of delivery. Accurate prediction of weight using prenatal
ultrasound is challenging as it requires images of specific fetal body parts
during advanced pregnancy which is difficult to capture due to poor quality of
images caused by the lack of amniotic fluid. As a consequence, predictions
which rely on standard methods often suffer from significant errors. In this
paper we propose the Residual Transformer Module which extends a 3D
ResNet-based network for analysis of 2D+t spatio-temporal ultrasound video
scans. Our end-to-end method, called BabyNet, automatically predicts fetal
birth weight based on fetal ultrasound video scans. We evaluate BabyNet using a
dedicated clinical set comprising 225 2D fetal ultrasound videos of pregnancies
from 75 patients performed one day prior to delivery. Experimental results show
that BabyNet outperforms several state-of-the-art methods and estimates the
weight at birth with accuracy comparable to human experts. Furthermore,
combining estimates provided by human experts with those computed by BabyNet
yields the best results, outperforming either of other methods by a significant
margin. The source code of BabyNet is available at
https://github.com/SanoScience/BabyNet.
