# Automated interpretation of prenatal ultrasound using a predefined acquisition protocol in resource-limited countries

## TL;DR

This study demonstrates that combining a simple, teachable ultrasound protocol with AI algorithms enables non-expert health workers in resource-limited settings to automatically assess fetal health indicators like gestational age and fetal presentation.

## Contribution

It introduces a standardized ultrasound acquisition protocol and AI-based analysis that can be taught quickly and used by non-specialists to automatically interpret prenatal ultrasound data.

## Key findings

- Automated detection of fetal presentation and twin pregnancies.
- Accurate estimation of gestational age from ultrasound images.
- Protocol can be learned within two hours by healthcare workers.

## Abstract

In this study, we combine a standardized acquisition protocol with image analysis algorithms to investigate if it is possible to automatically detect maternal risk factors without a trained sonographer. The standardized acquisition protocol can be taught to any health care worker within two hours. This protocol was acquired from 280 pregnant women at St.\ Luke's Catholic Hospital, Wolisso, Ethiopia. A VGG-like network was used to perform a frame classification for each frame within the acquired ultrasound data. This frame classification was used to automatically determine the number of fetuses and the fetal presentation. A U-net was trained to measure the fetal head circumference in all frames in which the VGG-like network detected a fetal head. This head circumference was used to estimate the gestational age. The results show that it possible automatically determine gestational age and determine fetal presentation and the potential to detect twin pregnancies using the standardized acquisition protocol.