# On-device fetal ultrasound assessment with TensorFlow Lite

DevFeed: [On-device fetal ultrasound assessment with TensorFlow Lite](<https://devfeed.tech/articles/on-device-fetal-ultrasound-assessment-with-tensorflow-lite-7372.md>)

Original publisher: [Read original article](<https://blog.tensorflow.org/2023/06/on-device-fetal-ultrasound-assessment-with-tensorflow-lite.html>)

Author: TensorFlow Blog (noreply@blogger.com)

Published: 2023-06-20T16:00:00Z

Content type: article

Language: en

Sources: [The TensorFlow Blog](<https://devfeed.tech/sources/the-tensorflow-blog.md>)

Topics: [TensorFlow Lite](<https://devfeed.tech/topics/tensorflow-lite.md>), [Google](<https://devfeed.tech/topics/google.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Mobile](<https://devfeed.tech/topics/mobile.md>), [Protocol (disambiguation)](<https://devfeed.tech/topics/protocol.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [devices](<https://devfeed.tech/tags/devices.md>), [google](<https://devfeed.tech/tags/google.md>), [health](<https://devfeed.tech/tags/health.md>), [healthcare](<https://devfeed.tech/tags/healthcare.md>), [mobile](<https://devfeed.tech/tags/mobile.md>), [on-device](<https://devfeed.tech/tags/on-device.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [predictions](<https://devfeed.tech/tags/predictions.md>), [protocol](<https://devfeed.tech/tags/protocol.md>), [smartphones](<https://devfeed.tech/tags/smartphones.md>), [tensorflow-lite](<https://devfeed.tech/tags/tensorflow-lite.md>)

## AI overview

This article explains how TensorFlow Lite and Google Research AI models are being used with portable, smartphone-integrated ultrasound devices to help health workers assess gestational age and fetal presentation in under-resourced settings. The models analyze ultrasound video collected through a blind sweep protocol and are intended to expand access to clinically useful prenatal screening.

## Source excerpt

Posted by Angelica Willis and Akib Uddin, Health AI Team, Google Research How researchers at Google are working to expand global access to maternal healthcare with the help of AI TensorFlow Lite* is an open-source framework to run machine learning models on mobile and edge devices. It's popular for use cases ranging from image classification, object detection, speech recognition, natural language tasks, and more. From helping parents of deaf children learn sign language, to predicting air quality, projects using TensorFlow Lite are demonstrating how on-device ML could directly and positively impact lives by making these socially beneficial applications of AI more accessible, globally. In this post, we describe how TensorFlow Lite is being used to help develop ultrasound tools in under-resourced settings. Motivation According to the WHO, complications from pregnancy and childbirth contribute to roughly 287,000 maternal deaths and 2.4 million neonatal deaths worldwide each year. As many as 95% of these deaths occur in under-resourced settings and many are preventable if detected early. Obstetric diagnostics, such as determining gestational age and fetal presentation, are important indicators in planning prenatal care, monitoring the health of the birthing parent and fetus, and determining when intervention is required. Many of these factors are traditionally determined by ultrasound. Advancements in sensor technology have made ultrasound devices more affordable and portable, integrating directly with smartphones. However, ultrasound requires years of training and experience, and, in many rural or underserved regions, there is a shortage of trained ultrasonography experts, making it difficult for people to access care. Due to this global lack of availability, it has been estimated that as many as two-thirds of pregnant people in these settings do not receive ultrasound screening during pregnancy. Expanding access by enabling non-experts Google Research is building AI m