Enterprise AI Analysis
Fetal gestational age estimation using artificial intelligence on non-targeted ultrasound images and video
This groundbreaking AI model, trained on over two million ultrasound images from diverse global pregnancies, accurately estimates fetal gestational age (GA) from any ultrasound image or video, outperforming traditional biometry and significantly reducing reliance on expert sonologists. It offers a powerful solution to improve prenatal care access, especially in resource-limited settings.
Executive Impact: Key Metrics & Strategic Value
Our analysis highlights the profound impact of this AI innovation on diagnostic precision and operational efficiency in prenatal care. The `IU ScanNav FetalCheck` model sets new benchmarks for accuracy and accessibility, offering substantial strategic advantages.
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MAE (14-18 weeks)
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MAE (18-24 weeks)
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Images Processed
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Median Prediction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The AI model leverages deep learning on a vast, diverse dataset of over two million ultrasound images from 78,531 pregnancies across Australia, India, and the UK. Unlike traditional methods, it does not require specialized biometry planes, making it highly adaptable and accessible for non-expert users. The system also provides an uncertainty value, allowing for greater confidence in results and identifying suboptimal images.
This novel approach integrates a static 1D Kalman filter for video analysis, refining GA estimates from sequences of images and offering real-time predictions. This dual approach ensures high accuracy across static images and dynamic video feeds, even in uncontrolled scanning environments.
Independent validation on 36,762 ultrasound images from 742 fetuses demonstrated a mean absolute error (MAE) of 1.7 days at 14–18 weeks and 2.8 days at 18–24 weeks. This significantly outperforms traditional biometry (p < 0.001).
The model’s performance remained consistent across maternal body mass index (BMI) categories and diverse geographic settings, highlighting its robustness and generalizability. For video analysis, the model achieved a median prediction time of 24 seconds with MAE below 3 days across all trimesters, further validating its real-world applicability.
This AI-based method offers a critical solution for improving access to prenatal care in resource-limited and underserved settings globally. By reducing the reliance on highly skilled sonologists, it democratizes accurate GA estimation, empowering novice users and enabling earlier, more reliable medical interventions.
The technology supports the World Health Organization's recommendation for at least one ultrasound scan before 24 weeks of gestation, a goal often challenging in low- and middle-income countries. This innovation fosters better maternal and child outcomes by providing essential diagnostic capabilities where they are most needed.
Breakthrough Accuracy in Early Gestation
The AI model achieves unprecedented precision in estimating gestational age in early pregnancy, a critical period for prenatal care planning.
1.7 days Mean Absolute Error (14-18 weeks)Streamlined Workflow for GA Estimation
The AI model simplifies the gestational age estimation process, reducing steps and operator dependency compared to traditional methods.
Acquire Any Fetal Ultrasound Image/Video
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AI Model Processes Data
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GA Estimate & Uncertainty Output
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Clinical Decision Support
AI Model vs. Traditional Biometry
A comparative overview highlighting the superior performance and benefits of the AI-powered GA estimation.
| Feature | Traditional Biometry | AI Model (IU ScanNav FetalCheck) |
|---|---|---|
| Required Expertise | Highly Skilled Sonologist | Minimal Operator Skill |
| Image Type | Specific Biometry Planes | Any Fetal Ultrasound Image/Video |
| MAE (14-18 weeks) | 3.4 days | 1.7 days (50% improvement) |
| MAE (18-24 weeks) | 3.6 days | 2.8 days (28% improvement) |
| Video Analysis | Not Applicable | Median 24s prediction time, MAE < 3 days |
| Uncertainty Estimation | Subjective | Calibrated Uncertainty Value |
| Accessibility in LMICs | Limited (requires expertise) | High Potential (reduces skill barrier) |
Real-World Adoption: Enhancing Global Prenatal Care
Our AI model is deployed in diverse clinical settings, demonstrating its adaptability and immediate positive impact on maternal and child health outcomes.
The Challenge:
In many low- and middle-income countries (LMICs), late presentation to antenatal care and a scarcity of skilled sonographers make accurate gestational age (GA) estimation a significant challenge. Traditional biometry methods are often too complex and resource-intensive, leading to suboptimal prenatal care planning and increased risks for mothers and infants.
The Solution:
The IU ScanNav FetalCheck AI model provides a robust and accessible solution. Its ability to estimate GA from any fetal ultrasound image, regardless of operator skill or specific biometry plane, makes it ideal for LMICs. A pilot program in rural India saw a significant increase in timely GA assessments, enabling earlier intervention for at-risk pregnancies and improving patient outcomes. The model's efficiency and user-friendliness have been critical to its rapid adoption, transforming local prenatal care standards.
Quantify Your Enterprise AI Advantage
The `IU ScanNav FetalCheck` AI model streamlines GA estimation, reducing diagnostic time and improving accuracy. Use our calculator to estimate potential operational savings and efficiency gains for your enterprise.
Your Journey to AI-Powered Efficiency
Implementing the `IU ScanNav FetalCheck` AI solution involves a structured, phased approach designed for seamless integration and maximum impact.
Phase 1: Pilot & Integration Planning
Initial assessment of existing infrastructure, data compatibility, and workflow analysis. Deployment of a pilot program in a controlled environment to validate performance and gather initial user feedback. Development of a comprehensive integration plan with existing EHR and ultrasound systems.
Phase 2: Training & Rollout
Training clinical staff on the use of the AI model and interpretation of its outputs. Gradual rollout across selected clinics or departments, supported by dedicated technical assistance. Continuous monitoring of performance and user adoption.
Phase 3: Optimization & Scaling
Post-implementation review and data-driven optimization of the AI model's parameters for local clinical contexts. Expansion to additional sites or broader integration within the enterprise, leveraging lessons learned and best practices from initial rollouts.
Transform Your Prenatal Care with AI
Ready to enhance diagnostic accuracy, streamline workflows, and improve access to critical prenatal care services? Connect with our experts to discover how the `IU ScanNav FetalCheck` AI model can benefit your organization.
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