AI-DRIVEN FETAL GA ESTIMATION
Revolutionizing Prenatal Care with Non-Targeted Ultrasound AI
Our deep learning model, trained on over two million diverse ultrasound images, provides highly accurate gestational age (GA) estimates directly from any fetal ultrasound image or video, significantly outperforming traditional biometry and requiring minimal operator skill. This breakthrough paves the way for enhanced prenatal care accessibility globally, especially in underserved regions.
Executive Impact & Core Metrics
The deployment of AI for fetal gestational age estimation marks a significant leap forward in prenatal care, offering precision, speed, and accessibility previously unattainable. Explore the core metrics demonstrating this transformative potential.
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MAE (14-18 Weeks)
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Median Prediction Time
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Images in Training Dataset
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P-value vs. Traditional Biometry
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 trained on an unprecedented scale of diverse ultrasound data. It outputs not only a GA estimate but also an uncertainty value, crucial for clinical confidence. The integration of a Kalman filter refines predictions from video streams.
1.7 Days
Mean Absolute Error (14-18 Weeks)
The AI model achieved superior accuracy in early gestation, significantly outperforming traditional biometry (MAE 1.7 days at 14-18 weeks, 2.8 days at 18-24 weeks).
Intelligent GA Prediction Workflow
Image/Video Input
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Deep Learning Prediction (μ, σ)
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Uncertainty Filtering (Kalman)
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Confident GA Estimate
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Clinical Report
Independent validation across diverse datasets confirmed the AI model's robust performance. It consistently outperformed traditional biometry across various gestational age bands, maternal BMI categories, and geographic settings, demonstrating its clinical superiority.
| Metric | AI Model (Our Study) | Traditional Biometry (Literature) |
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| MAE (14-18 weeks) |
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| MAE (18-24 weeks) |
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| Operator Skill Required |
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| Video Analysis |
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| Consistency across BMI & Geography |
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24 Seconds
Median Prediction Time (Video Analysis)
The model produced a sufficiently confident GA estimate from video streams within a median of 24 seconds, with 95% of predictions completed under 60 seconds.
The AI model's ability to estimate GA from non-targeted ultrasound images significantly lowers the barrier to accurate prenatal care. This is particularly impactful for low- and middle-income countries (LMICs) and underserved populations, enabling novice users to perform reliable assessments.
Case Study: Empowering Remote Clinics with AI-driven GA
Problem: In many LMICs, late presentation to antenatal care and scarcity of skilled sonographers lead to inaccurate or absent GA estimation, hindering timely medical interventions and worsening maternal-fetal outcomes.
Solution: By deploying the AI model on portable ultrasound devices, even novice healthcare workers can obtain reliable GA estimates from non-targeted scans. This democratizes access to a critical diagnostic capability.
Impact: Pilot programs in rural settings showed a significant increase in early-gestation GA assessments, enabling proactive management of pregnancies and a reduction in adverse birth events.
Streamlined Prenatal Care Adoption Pathway
Basic Ultrasound Scan
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AI-Powered GA Analysis
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Instant Accurate GA Report
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Informed Clinical Decision
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Improved Health Outcomes
Quantify Your Enterprise AI Savings
Estimate the potential efficiency gains and cost savings by integrating AI-powered GA estimation into your healthcare operations. Adjust the parameters to reflect your organization's scale.
Your Enterprise AI Roadmap
Implementing AI for critical diagnostics requires a strategic, phased approach to ensure seamless integration and maximum impact. Our roadmap outlines a clear path from pilot to widespread adoption.
Phase 1: Pilot & Validation (3-6 Months)
Establish a focused pilot program within a specific department or clinic. This includes data integration, initial model calibration to your specific environment, and validation against local clinical standards to build confidence.
Phase 2: Integration & Scaling (6-12 Months)
Expand the AI solution across more sites or departments. This involves optimizing workflows, training staff on the new system, and integrating with existing EMR/PACS systems for streamlined data flow and reporting.
Phase 3: Continuous Improvement (Ongoing)
Implement a framework for continuous monitoring and model refinement. This ensures the AI remains up-to-date with evolving clinical practices and data, maximizing long-term accuracy and operational benefits.
Ready to Transform Prenatal Care?
Our AI-driven solution offers an unparalleled opportunity to enhance the accuracy, accessibility, and efficiency of gestational age estimation. Partner with us to bring this revolutionary technology to your institution.
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