AI-Powered Prenatal Ultrasound Interpretation
Prudent Partners annotated fetal structures in ultrasound scans to train a maternal health AI model. The solution improved diagnostic accuracy and supported early anomaly detection in prenatal care.
Case Details
Clients: Pixel Art Company
Start Day: 13/01/2024
Tags: Marketing, Business
Project Duration: 9 Month
Client Website: Pixelartteams.com
Let’s Work Together for Development
Call us directly, submit a sample or email us!
Executive Summary
A company focused on maternal health partnered with an annotation team to train AI models capable of interpreting prenatal ultrasound images. Thousands of scans were annotated with bounding boxes, keypoints, and classification tags to identify fetal anatomy, positioning, and potential complications. The project aimed to democratize expert-level prenatal screening and enable early detection of birth anomalies, even in underserved regions.
Introduction
Background
Prenatal ultrasound scans are vital for monitoring fetal development, but interpreting them requires specialized expertise. Variations in scan quality, fetal positioning, and limited access to trained technicians often result in missed or delayed diagnoses. The company aimed to train AI to “read” ultrasounds with human-level precision, supporting early detection of fetal issues and guiding patient care.
Industry
Maternal Health / Prenatal Imaging / Medical AI
Tools Used
Client-developed annotation platform supporting bounding boxes, keypoints, and metadata tagging
Products/Services
Image annotation services for training AI to identify fetal limbs, organs, nervous system markers, and positional orientation in diverse ultrasound scenarios
Challenge
Problem Statement
AI models struggled with scans where the fetus was in unusual positions or when image quality was reduced due to motion blur or shadows.
Impact
- Missed or inaccurate diagnoses of fetal complications
- Increased risk to both parent and baby
- Critical in low-resource settings where AI may be relied upon in the absence of specialists
Solution
Overview
An intensive annotation effort was introduced to capture detailed fetal anatomy and positioning across a wide range of ultrasound images.
Implementation Approach
- Annotated each image with bounding boxes marking fetal position
- Used keypoints to identify specific body parts, including limbs, organs, and nervous system structures
- Applied tags to differentiate singleton and multiple gestations
- Included varied fetal orientations to improve model robustness
- Followed medical imaging best practices to preserve clinical context
- Maintained consistency through QA cycles and reviewer consensus
Tools & Resources Used:
- Client-side annotation platform with medical imaging support
- In-house QA protocols and fetal anatomy guides
- Collaboration with clinicians and trained annotators
Results
Outcome
The annotated dataset significantly enhanced the AI model’s ability to interpret diverse ultrasound scenarios, accurately extracting fetal health indicators even under suboptimal imaging conditions.
Benefits
- Increased Accuracy: Stronger performance in detecting key fetal structures
- Improved Equity in Care: Enabled under-resourced clinics to offer near-specialist-level screening
- Time-Saving: Faster interpretation and triaging for high-risk pregnancies
- Scalable Solution: Annotation protocol can be extended to other imaging types and pregnancy stages
Conclusion
Summary
Detailed ultrasound annotation enabled AI to assist clinicians in early identification of complications, reducing strain on healthcare systems and supporting better maternal outcomes, particularly in areas with limited access to care.
Future Plans
- Expand AI capabilities to other fetal imaging types
- Integrate decision support for high-risk cases
- Track fetal growth trends and anomaly detection across gestational stages
Call to Action
Healthcare institutions, telemedicine platforms, and maternal care innovators can adopt structured medical annotation approaches or collaborate with AI development partners to accelerate the creation of diagnostic-grade AI for prenatal imaging—bringing equity and excellence in care worldwide