Worldwide, 99% of all maternal deaths occur in developing countries. Ultrasound can be used
to detect maternal risk factors, but this technique is rarely used in developing countries
because it is too expensive, and it requires a trained sonographer to acquire and interpret the
ultrasound images. In this work we use a low-cost ultrasound device which was combined
with the obstetric sweep protocol (OSP) and deep learning algorithms to automatically detect
maternal risk factors. The OSP can be taught to any health care worker without prior
knowledge of ultrasound within one day, so there is no need for a trained sonographer.
The OSP was acquired from 318 pregnant women using the low-cost MicrUs (Telemed
Ultrasound Medical Systems, Milan, Italy) in Ethiopia. Two deep learning networks and two
random forest classifiers were trained to automatically detect twin pregnancies, estimate
gestational age (GA) and determine fetal presentation. The first deep learning network
performs a frame classification, which was used to automatically separate the six sweeps of
the OSP and automatically detect the fetal head and torso. The second deep learning network
was trained to measure the fetal head circumference (HC) using all frames in which the first
deep learning system detected the fetal head. The HC was used to determine the GA. Two
random forest classifiers were trained to detect twin pregnancies and determine fetal
presentation using the frame classification of the first deep learning network.
The developed algorithm can automatically estimate the GA with an interquartile range of
15.2 days, correctly detected 61% of all twins with a specificity of 99%, and correctly detect
all 31 breech presentations and 215 of the 216 cephalic presentations. The developed
algorithm can be computed in less than two seconds, making real-time application feasible.
The presented system is able to determine three maternal risk factors using the OSP. The OSP
can be acquired without the need of a trained sonographer, which makes widespread obstetric
ultrasound affordable and fast to implement in resource-limited settings. This makes is
possible to refer pregnant women in time to a hospital to receive treatment when risk factors
are detected.