No sonographer, no radiologist: New system for automatic prenatal detection of fetal biometry, fetal presentation, and placental location

PLoS One. 2022 Feb 9;17(2):e0262107. doi: 10.1371/journal.pone.0262107. eCollection 2022.

Abstract

Ultrasound imaging is a vital component of high-quality Obstetric care. In rural and under-resourced communities, the scarcity of ultrasound imaging results in a considerable gap in the healthcare of pregnant mothers. To increase access to ultrasound in these communities, we developed a new automated diagnostic framework operated without an experienced sonographer or interpreting provider for assessment of fetal biometric measurements, fetal presentation, and placental position. This approach involves the use of a standardized volume sweep imaging (VSI) protocol based solely on external body landmarks to obtain imaging without an experienced sonographer and application of a deep learning algorithm (U-Net) for diagnostic assessment without a radiologist. Obstetric VSI ultrasound examinations were performed in Peru by an ultrasound operator with no previous ultrasound experience who underwent 8 hours of training on a standard protocol. The U-Net was trained to automatically segment the fetal head and placental location from the VSI ultrasound acquisitions to subsequently evaluate fetal biometry, fetal presentation, and placental position. In comparison to diagnostic interpretation of VSI acquisitions by a specialist, the U-Net model showed 100% agreement for fetal presentation (Cohen's κ 1 (p<0.0001)) and 76.7% agreement for placental location (Cohen's κ 0.59 (p<0.0001)). This corresponded to 100% sensitivity and specificity for fetal presentation and 87.5% sensitivity and 85.7% specificity for anterior placental location. The method also achieved a low relative error of 5.6% for biparietal diameter and 7.9% for head circumference. Biometry measurements corresponded to estimated gestational age within 2 weeks of those assigned by standard of care examination with up to 89% accuracy. This system could be deployed in rural and underserved areas to provide vital information about a pregnancy without a trained sonographer or interpreting provider. The resulting increased access to ultrasound imaging and diagnosis could improve disparities in healthcare delivery in under-resourced areas.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Female
  • Humans
  • Placenta*
  • Pregnancy

Grants and funding

The authors acknowledge the following funding sources: Innóvate Peru (409-FIDECOM INNOVATEPERU-PVE-2017) and Pontifical Catholic University of Peru (Períodos de Investigación 2020) from Benjamin Castaneda. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. At the time of the study, Miguel Egoavil, Lorena Tamayo, and Benjamin Castaneda were employed by the company Medical Innovation and Technology which seeks to bring ultrasound to rural areas. The funder provided support in the form of salaries for authors ME, LT, and BC, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section. They only provided support in the form of author salaries and access to the ultrasound imaging used in the study.