  Objective. This work investigates the use of deep convolutional neural
networks (CNN) to automatically perform measurements of fetal body parts,
including head circumference, biparietal diameter, abdominal circumference and
femur length, and to estimate gestational age and fetal weight using fetal
ultrasound videos. Approach. We developed a novel multi-task CNN-based
spatio-temporal fetal US feature extraction and standard plane detection
algorithm (called FUVAI) and evaluated the method on 50 freehand fetal US video
scans. We compared FUVAI fetal biometric measurements with measurements made by
five experienced sonographers at two time points separated by at least two
weeks. Intra- and inter-observer variabilities were estimated. Main results. We
found that automated fetal biometric measurements obtained by FUVAI were
comparable to the measurements performed by experienced sonographers The
observed differences in measurement values were within the range of inter- and
intra-observer variability. Moreover, analysis has shown that these differences
were not statistically significant when comparing any individual medical expert
to our model. Significance. We argue that FUVAI has the potential to assist
sonographers who perform fetal biometric measurements in clinical settings by
providing them with suggestions regarding the best measuring frames, along with
automated measurements. Moreover, FUVAI is able perform these tasks in just a
few seconds, which is a huge difference compared to the average of six minutes
taken by sonographers. This is significant, given the shortage of medical
experts capable of interpreting fetal ultrasound images in numerous countries.
