Cardio Computing provides an automated GPU‑accelerated platform that creates patient‑specific procedural plans from CT scans in under 45 seconds. It combines deep‑learning segmentation with finite‑element biomechanics to predict device deployment and generate DICOM‑compatible reports for interventional cardiology, cardiac surgery, and medical‑device R&D.
Funding
Funding not disclosed
Founders
Product
Problem
Percutaneous cardiovascular interventions lack a standardized, quantitative pre‑operative planning workflow. Current practices rely on manual image interpretation, creating operator‑dependent variability and increasing the risk of periprocedural complications. The absence of automated, reproducible analysis hampers both patient outcomes and efficient medical‑device development.
Solution
Cardio Computing delivers a fully automatic, GPU‑accelerated platform that generates personalized procedural plans from CT datasets in under 45 seconds. The system combines generative pre‑trained neural networks for high‑resolution anatomical segmentation with physics‑based numerical biomechanics to simulate device deployment and predict post‑procedural geometry. Results are exported as standardized reports and DICOM‑compatible files, enabling seamless integration into existing clinical PACS and device‑design pipelines. By providing objective, reproducible metrics, the platform supports value‑based healthcare initiatives and accelerates R&D cycles for minimally‑invasive cardiovascular devices. The solution is designed for real‑time use in catheterization labs and can be scaled across multi‑center networks without additional hardware investment.
Target Audience
Primary users are interventional cardiologists, cardiac surgeons, and imaging specialists who plan percutaneous valve and aortic interventions, as well as medical‑device manufacturers and R&D teams seeking rapid, data‑driven design validation.
Features
- GPU‑optimized inference engine delivering end‑to‑end analysis (segmentation, measurement, simulation) in <45 seconds per case
- Generative pre‑trained deep‑learning models for automated 3‑D reconstruction of aortic root, thoracic aorta, and right‑ventricular outflow tract from CT images
- Finite‑element biomechanics module that predicts device‑tissue interaction, optimal sizing, and post‑deployment geometry for TAVI, TEVAR, and pulmonary valve implantation
- Automated extraction of clinically relevant metrics (diameters, curvature, landing zones) with built‑in quality‑control checks and uncertainty quantification
- DICOM‑compliant output and HL7/FHIR API for integration with hospital PACS, EHRs, and device‑manufacturer design tools
- Secure, encrypted cloud storage and audit‑trail logging to meet HIPAA and GDPR requirements
- Batch‑processing capability for large‑scale clinical studies and device R&D simulations