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Saving lives with data by providing regulatory-grade safety and effectiveness data.

AI-powered retinal scanning for non-invasive cardiovascular and systemic disease risk prediction.

Mediwhale is a pioneer in the AI-driven health diagnostics space, specifically focusing on the retina as a non-invasive window into systemic health. Their flagship platform, Reti-Intelligence (including Reti-CVD and Reti-CKD), utilizes deep learning algorithms trained on massive longitudinal datasets, such as the UK Biobank, to predict cardiovascular and kidney disease risks with accuracy levels comparable to traditional, invasive tests like Coronary Artery Calcium (CAC) scoring. By 2026, Mediwhale has positioned itself as a critical bridge between ophthalmology and preventive primary care, enabling clinicians to assess systemic risks within seconds using standard fundus cameras. The technical architecture is built on a proprietary convolutional neural network (CNN) stack that analyzes microvascular changes in the retina—indicators often invisible to the human eye. This allows for early intervention in high-risk patients who might otherwise bypass traditional screening due to cost or radiation concerns associated with CT scans. The solution is designed for seamless integration into existing hospital EHR systems and diagnostic workflows via DICOM and HL7 standards, facilitating its role in large-scale population health management and corporate wellness programs.
Mediwhale is a pioneer in the AI-driven health diagnostics space, specifically focusing on the retina as a non-invasive window into systemic health.
Explore all tools that specialize in estimate biological age. This domain focus ensures Mediwhale delivers optimized results for this specific requirement.
Explore all tools that specialize in risk assessment. This domain focus ensures Mediwhale delivers optimized results for this specific requirement.
Uses specialized CNNs to detect sub-clinical cardiovascular risk from standard retinal images.
Real-time image quality validation using AI to ensure scans are diagnostic-grade before processing.
Algorithm that calculates biological age versus chronological age based on retinal vascular health.
Combines retinal data with basic clinical inputs (age, sex, smoking status) for enhanced predictive power.
Full compliance with medical imaging standards for PACS integration.
Specialized model for detecting signs of Chronic Kidney Disease through retinal microvasculature.
Hardware-agnostic AI engine that normalizes images from various fundus camera manufacturers.
Clinical site assessment and fundus camera compatibility check.
Selection of deployment model (Cloud-based or On-premise bridge).
Installation of the Mediwhale Gateway software for secure data transmission.
Integration with local Electronic Health Record (EHR) systems via HL7/FHIR.
Configuration of DICOM nodes for automatic image routing from camera to AI engine.
User authentication setup via OAuth2 or SSO for clinical staff.
Calibration and test scans to verify image quality and processing latency.
Clinical staff training on interpreting Reti-CVD risk scores and reports.
Workflow optimization for point-of-care patient counseling.
Go-live and monitoring of API call performance and diagnostic accuracy.
All Set
Ready to go
Verified feedback from other users.
"Highly praised by clinicians for its speed and non-invasive nature, with specific accolades for its CVD prediction AUC scores which rival invasive benchmarks."
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