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BabyChecker

Artificial Intelligence for Pregnancy Screening with Ultrasound

BabyChecker AI analyses obstetric ultrasound scans to identify risks in pregnancy. Scans can be easily acquired with a handheld ultrasound and by any frontline health worker within 2 minutes.

3-minute training video

Functions offline

No prior experience

No constant power required

How It Works

The user is guided by the BabyChecker mobile application to perform a scan, which consists of standard sweeps across the abdomen. Once the sweeps are completed, AI analyses the scan and provides outputs for gestational age, foetal presentation, and placenta localisation. After the scan is analysed remotely, outputs for multiple gestations and a 2D foetal image will be shown on the app. BabyChecker can help identify potential risks, improve maternal and antenatal care, and provide timely referrals from primary healthcare facilities.

Everything in one bag

The BabyChecker comes in a protective case crossbody bag and contains all the essential parts needed for immediate screening:

01. Crossbody bag

02. Ultrasound probe

03. Android smartphone

04. Mobile application

05. User instruction card

Partners & Collaborators

Our BabyChecker tool is not just an innovative concept—it’s already making a difference at community health centres in various countries where maternal mortality is a problem. These initiatives represent our unwavering commitment to improving maternal health outcomes.

Sierra Leone

Collaborators

Lion Heart Foundation

Jericho Road Community Health Centre

Tonkolili District

The Mansaray Foundation

Initiative for Global Perinatal Care

Tanzania

Collaborators

Misingi

Karatu District

Honduras

Collaborators

UNFPA

Ministry of Health

Zambia

Collaborator

Dawa Health

Ghana

Collaborator

TREATS Association

Guatemala

Collaborator

Fundación EHAS

We scanned a woman using BabyChecker recently during our open-air clinic session. She was due in about a week or two. This was her first scan with BabyChecker; it showed she had placenta previa. According to her, she didn’t have that information, and there was no record of the scan that she’d gone to the hospital. With this information, we were able to refer her to the hospital.”

Collaborator Stories

We appreciate the esteemed speakers who shared their insights on BabyChecker, enriching our understanding of healthcare challenges in marginalized regions and how AI-assisted POCUS can help overcome them.

Karlin Bacher – Using Technology to Leapfrog Gaps Present in Global Health

Sofia Sappia – Automatic detection of risky pregnancies in low-resource settings: developing the BabyChecker

Irene de Vries – Beyond Medical Effectiveness: What Patients, Providers, and Health Systems Need

Jose Manuel Perez – The BabyChecker Journey in Honduras

Blogs