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Session: Birth & Neonates
Session starts: Thursday 24 January, 13:30
Presentation starts: 14:15
Room: Lecture room 558

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Thomas van den Heuvel (Radboud university medical center)
Bram van Ginneken (Radboud university medical center)
Chris de Korte (Radboud university medical center)

Abstract:
Worldwide, 99% of all maternal deaths occur in developing countries. Ultrasound is normally used to detect maternal risk factors, but it is rarely available in developing countries because it is too expensive, and it requires a trained sonographer to acquire and interpret the ultrasound images. 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. 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 presence of the fetal head and torso in each frame. The second deep learning network was trained to measure the head circumference (HC) using all frames in which the first deep learning system detected the presence of a 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 analysis of the OSP 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.