RADiCAIT develops Insilico PET®, an AI solution that transforms standard CT scans into functional, PET-like maps. This technology provides actionable physiological insights for disease detection and staging without requiring radioactive tracers or new hardware. The platform delivers PET-level diagnostic accuracy at the scale and speed of existing CT infrastructure.
Funding
$300K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Standard CT scans provide anatomical data but lack the physiological insights necessary for accurate disease detection, staging, and treatment planning. Positron Emission Tomography (PET) offers functional information but is limited by its scarcity, cost, and the need for radioactive tracers. This creates a gap in diagnostic capabilities, particularly for conditions like cardiological, neurological, and oncological diseases.
Solution
RADiCAIT's Insilico PET® technology addresses this diagnostic gap by leveraging artificial intelligence to generate PET-like functional maps from conventional CT scans. This AI-driven approach eliminates the requirement for radioactive tracers, additional hardware, or separate patient appointments, thereby enhancing the utility of existing imaging infrastructure. The platform provides clinicians with actionable physiological data, enabling more precise disease detection, staging, and treatment strategy development. Insilico PET® offers a fast, safe, and scalable method to augment diagnostic accuracy and improve patient care pathways.
Target Audience
The primary target audience includes radiologists, oncologists, cardiologists, neurologists, and healthcare institutions seeking to enhance diagnostic imaging capabilities without increasing procedural complexity or cost.
Features
- AI-powered image synthesis to generate functional maps from standard CT datasets.
- Proprietary deep learning models trained on extensive radiological and physiological data.
- Integration with existing PACS and radiology workflows without requiring new hardware.
- Output provides quantitative physiological metrics comparable to PET imaging.
- Enables non-invasive assessment of disease activity and metabolic processes.
- Facilitates improved diagnostic confidence and treatment stratification.