Traditional intraoperative imaging techniques often rely on costly hardware like CT scanners, expose patients to high levels of radiation, and can disrupt surgical workflows. Pre-operative scans may not accurately reflect the patient's anatomy during surgery, and real-time surgical navigation can be limited by line-of-sight requirements and cumbersome equipment.

See All AI develops an AI-powered platform that generates high-resolution, sliceable 3D models from standard 2D C-arm fluoroscopic X-rays, eliminating the need for pre-operative CTs or additional intraoperative imaging systems. The platform leverages deep learning and machine vision to create 3D volumes with 1mm cross-sectional slices from as few as two fluoroscopic images. This enables real-time surgical planning and visualization with fidelity comparable to CT, but with reduced radiation exposure, lower costs, and a smaller capital footprint. The system is designed to integrate into existing operating room environments without disrupting surgeon techniques or workflow, and is compatible with standard surgical draping, shielding, and positioning.

AI-based platform transforms 2D fluoroscopic X-rays into navigable 3D volumes with 1mm cross-sectional slices.

"Track All" visible light spectrum camera delivers sub-millimeter tracking accuracy for surgical instruments without markers, emitters, or direct line-of-sight to anatomy.

Compatible with existing fluoroscopy units, transforming them into an integral component of a 3D imaging and navigation solution.

Designed for minimally invasive surgery (MIS) workflows.

High-performance, ultra-efficient camera with onboard AI computing.

Open, software-based approach to imaging, eliminating the need for expensive hardware or closed ecosystems.

The primary target audience includes surgeons and hospitals seeking to improve intraoperative visualization and navigation while reducing radiation exposure and costs associated with traditional 3D imaging techniques.