Best technical abstract – Thomas LA van den Heuvel

Thomas van den Heuvel is a postdoctoral fellow at the Radboud university medical center in the Netherlands. His research focuses on combining point-of-care ultrasound devices with deep learning solutions to facilitate diagnosis in resource limited countries. During his PhD, he developed a deep learning software that automatically detects twin pregnancies, estimates gestational age and determines fetal presentation. The system is so easy to operate that any health care worker can use the device after two hours of training. Currently, the methods are implemented in Android based smartphones to further facilitate widespread use of this technology.

Publikationsverlauf

Artikel online veröffentlicht:
04. August 2020

• References

• 1 World Health Organization, UNICEF, UNFPA, The World Bank and the United Nations Population Division, Trends in maternal mortality: 1990 to 2013: estimates by WHO, UNICEF, UNFPA, The World Bank and the United Nations Population Division. 2014
• 2 World Health Organization. WHO recommendations on antenatal care for a positive pregnancy experience,. World Health Organization 2016
• 3 DeStigter KK, Morey GE, Garra BS. et al Low-cost teleradiology for rural ultrasound,. in IEEE Glob Humanit Technol Conf 2011
• 4 van den Heuvel TLA, de Bruijn D, de Korte CL. et al. Automated measurement of fetal head circumference using 2D ultrasound images. PloS One 2018; 13 (08) e0200412
• 5 van den Heuvel TLA, Petros H, Santini S. et al Automated fetal head detection and circumference estimation from free-hand ultrasound sweeps using deep learning in resource-limited countries. Ultrasound in Medicine & Biology 2018
• 6 van den Heuvel TLA. Automated low-cost ultrasound: improving antenatal care in resource-limited settings. Universiteitsbibliotheek Nijmegen 2019
• 7 van den Heuvel TLA, de Bruijn D, de Korte CL. et al Automated measurement of fetal head circumference using 2D ultrasound images [Data set]. Zenodo 2018