Delivery Date AI uses advanced artificial intelligence, specifically deep learning algorithms like convolutional neural networks and transformers, to analyze ultrasound images. The AI has been trained on over 8 million ultrasound images, allowing it to identify complex pixel patterns associated with gestational age and preterm birth risks. When an ultrasound scan is performed, the images are instantly sent to Delivery Date AI’s cloud-based platform, which processes them in real-time and generates a predicted delivery date. The system integrates seamlessly with existing ultrasound equipment, requiring no additional hardware, and delivers results within seconds.

Unlike traditional fetal biometry, which relies on specific measurements of fetal anatomy, Delivery Date AI utilizes a more comprehensive approach:

• Pixel-level Analysis: Examines individual pixels and patterns across the entire ultrasound image and identifies subtle features and textures not visible to the human eye.
• Maternal Anatomy Focus: Analyzes changes in maternal tissues and structures, such as cervical length, uterine wall thickness, and placental characteristics.
• Holistic Image Interpretation: Considers the entire ultrasound image as a complex data set, looking for patterns and relationships across different anatomical structures.
• Machine Learning Algorithms: Uses advanced AI techniques to identify correlations between image features and pregnancy outcomes, continuously improving predictions based on new data.
• Beyond Standard Measurements: Does not rely solely on traditional measurements like fetal crown-rump length or femur length and can detect risk factors not apparent in standard biometric measurements.

This approach allows Delivery Date AI to potentially capture a broader range of indicators for preterm birth risk and more accurate delivery date prediction.

Delivery Date AI can start predicting delivery dates as early as 6 weeks into pregnancy.

Based on the University of Kentucky validation study, Delivery Date AI demonstrates high accuracy in predicting delivery dates. For term births, the AI achieved an R² value of 0.95, indicating a very strong correlation between predicted and actual delivery dates. For term births plus spontaneous preterm births, the R² value was 0.94.

Delivery Date AI currently focuses on predicting preterm birth and delivery dates. However, Ultrasound AI has plans to expand its capabilities to detect other pregnancy complications in the future. The company’s product roadmap includes developing AI algorithms to predict conditions such as pre-eclampsia, placental abruption, intrauterine growth restriction (IUGR), and more accurate gestational age determination.

Delivery Date AI is designed to work with routine ultrasound scans performed during pregnancy, so there’s no need for additional or more frequent scans beyond what is typically recommended. The frequency of scans can follow standard obstetric care guidelines, which usually include scans at key stages of pregnancy. Each time an ultrasound is performed, Delivery Date AI can provide updated predictions, allowing for ongoing monitoring of the pregnancy’s progress and any changes in the risk of preterm birth.

The AI has been trained on a large dataset of over 8 million ultrasound images, which includes a diverse range of pregnancy types. The University of Kentucky study showed high accuracy across various subgroups, suggesting broad applicability. There are no stated exclusion criteria for the use of Delivery Date AI.

Delivery Date AI offers several advantages over traditional methods of predicting preterm birth:

• Earlier prediction: Delivery Date AI can provide accurate predictions as early as 6 weeks into pregnancy, which is significantly earlier than many traditional methods.
• Non-invasive: It uses standard ultrasound images, requiring no additional tests or procedures beyond routine prenatal care.
• Predicts an actual delivery date which can be used to assess the severity of a predicted preterm birth.

Delivery Date AI is compatible with most cart-based ultrasound machines manufactured within the last twenty years.

No, you do not need to purchase new equipment if your current ultrasound machines are compatible with Delivery Date AI’s requirements.

Delivery Date AI integrates seamlessly into existing workflows by:

• Connecting to ultrasound machines or PACS systems to automatically receive images.
• Processing images in real-time through a cloud-based platform and delivering results back within seconds.
• Requiring no additional hardware or specialized ultrasound views.

Delivery Date AI ensures patient data is secure by:

• Using robust encryption protocols during data transmission and storage.
• Storing data in secure, HIPAA-compliant servers.
• De-identifying patient data to protect confidentiality.

Yes, Delivery Date AI complies with all HIPAA regulations to ensure the security and privacy of patient data.

Yes, Delivery Date AI can store historical scan data, allowing healthcare providers to:

• Track pregnancy progress over time.
• Compare current scans with previous ones.
• Use data visualization tools for comprehensive analysis.

Connecting existing ultrasound machines or PACS systems to Delivery Date AI’s cloud-based platform typically takes only a few minutes. This also includes providing basic orientation to staff on how to access and interpret Delivery Date AI results.

Ultrasound AI provides technical support to ensure optimal functionality and user experience. This includes a dedicated support team to help with implementation, troubleshooting, and use of Delivery Date AI. Support also extends to initial setup, software updates, and resolving any issues that arise during use.

We offer the first 50 scans free which can be retrospective or prospective to allow users to verify that Delivery Date AI is accurate for their specific machines and patient population.

In the United States, Delivery Date AI is currently in the FDA De Novo submission phase, with expected clearance by Q1 2025. The product has received full regulatory approval in Brazil from ANVISA (National Health Surveillance Agency) and is exempt from regulatory approval in Chile.

The PAIR (Perinatal Artificial Intelligence in ultRasound) study conducted with the University of Kentucky validates Delivery Date AI’s effectiveness. Key details include:

• Study size: 5,714 pregnant women who delivered at the University of Kentucky between 2017 and 2021.
• Data volume: 19,940 unique ultrasound exams with known ground truth labels, and over 8 million total ultrasound images used for training the AI.
• Key findings: For term births, the AI achieved an R² value of 0.95. For term births plus spontaneous preterm births, the R² value was 0.94.

Further research and clinical studies are ongoing and in discussion around the world. If your institution is interested in participating, please reach out to us.

This is negotiated on a case-by-case basis depending on geographical location and volume. Please contact us directly for more information.

No, there are no additional costs besides the Delivery Date AI license fee.

Yes, please contact us at rd@ultrasound.ai for more information.

Insurance coverage is not currently available, but we are actively pursuing agreements with insurers to enable reimbursement.

Delivery Date AI improves patient outcomes in several key ways:

• Early detection: Accurately predicts preterm birth risks as early as 6 weeks into pregnancy, enabling timely interventions.
• Personalized care: Provides specific delivery date predictions, allowing for tailored prenatal care plans.
• Reduced complications: Early identification of high-risk pregnancies can lead to interventions that may reduce preterm birth rates and associated complications.

Yes, Delivery Date AI has the potential to help reduce healthcare costs by:

• Enabling early detection and intervention, which can prevent costly complications associated with preterm births.
• Reducing NICU stays, one of the major cost factors in preterm births.
• Providing accurate delivery date predictions that can optimize resource allocation and reduce unnecessary interventions.

Setting up Delivery Date AI involves:

• Connecting ultrasound machines or PACS systems to the cloud-based platform.
• Providing orientation for staff on how to access and interpret results.
• Ensuring seamless integration into existing workflows.

Yes, Ultrasound AI is actively working to expand Delivery Date AI’s capabilities. Planned developments include:

• Stillbirth prediction.
• Pre-eclampsia detection.
• Placental abruption prediction.
• Intrauterine growth restriction (IUGR) identification.
• More accurate gestational age determination.

Yes, Delivery Date AI can support national maternal health initiatives by:

• Providing aggregated data to track preterm birth rates and identify at-risk populations.
• Standardizing prenatal care with AI-driven predictions.
• Supporting public health research through anonymized data analytics.

Absolutely! Delivery Date AI can be customized to meet the specific requirements or regulations of your country. Please contact Ultrasound AI for further information and to discuss your needs.

Delivery Date AI prioritizes data security and patient confidentiality by:

• Using industry-standard encryption protocols to safeguard patient information during transmission and storage.
• Storing all data on HIPAA-compliant servers.
• Implementing rigorous de-identification processes to ensure patient privacy.

No special training is required to use Delivery Date AI. The system is designed to work seamlessly with standard ultrasound images and existing workflows. Basic orientation is provided to ensure users can access and interpret the results effectively.

Delivery Date AI is cloud-based, meaning updates and maintenance are managed centrally by Ultrasound AI. There is no need for frequent manual updates or on-site maintenance. The system ensures users always have access to the latest features and improvements.

Delivery Date AI provides aggregated, anonymized data that can inform public health policies, including:

• Preterm birth risk trends across different populations.
• Regional variations in pregnancy outcomes.
• Insights into the effectiveness of early interventions.
• Data to support resource allocation and improve maternal health strategies.

For a comprehensive list of available data points and their potential policy applications, it would be best to consult directly with Ultrasound AI. We can provide more specific information on the types of data their system can generate to support public health initiatives.