Medniscient AI provides a cloud‑native platform that applies deep‑learning to cardiac CT scans for automated segmentation, plaque quantification, and functional assessment, delivering quantitative reports through a web dashboard. The service integrates via HIPAA‑compliant APIs with PACS/RIS, offers an AI model that predicts TAVR procedural risk scores, and enables secure, real‑time data sharing across healthcare facilities.
Funding
Funding not disclosed
Founders
Product
Problem
Interpretation of cardiac CT and other medical imaging studies often requires time‑intensive manual review by specialists, leading to diagnostic delays and variability in accuracy. Additionally, imaging data are frequently siloed across disparate hospital systems, making secure, real‑time sharing difficult.
Solution
Medniscient AI delivers a cloud‑native platform that applies deep‑learning algorithms to cardiac CT and related imaging datasets, generating quantitative assessments that support faster, more consistent clinical decisions. The service runs in a HIPAA‑compliant environment with end‑to‑end encryption, enabling instant, secure data exchange between facilities. An API‑first architecture allows integration with existing PACS workflows without requiring EMR connectivity, while a web‑based dashboard presents AI‑derived metrics and visualizations for radiologists and cardiologists. The AI TAVR module predicts peri‑procedural complications directly from CT scans, providing actionable risk scores that can be incorporated into surgical planning. Scalable cloud compute resources ensure rapid turnaround even for high‑volume imaging centers.
Target Audience
Primary customers are hospital radiology and cardiology departments, imaging centers, and specialty clinics that perform cardiac CT and require AI‑enhanced diagnostic support. The platform also serves surgical planning teams evaluating transcatheter aortic valve replacement (TAVR) candidates.
Features
- Convolutional neural network pipelines for automated cardiac CT segmentation, plaque quantification, and functional assessment
- AI TAVR predictive model that outputs complication risk scores based on volumetric and morphological CT features
- Fully HIPAA‑compliant cloud storage with AES‑256 encryption and role‑based access controls for patient data
- RESTful API and DICOM‑compatible endpoints for seamless integration with PACS and radiology information systems (RIS)
- Web dashboard with interactive heat‑maps, quantitative reports, and exportable PDF summaries for clinician review
- Instant, audit‑logged data sharing across multiple healthcare facilities via secure token‑based authentication
- No EMR integration required; the platform operates as a standalone service that can be layered onto existing imaging workflows
- Auto‑scaling compute infrastructure leveraging GPU‑accelerated inference to maintain low latency for high‑throughput environments