Arago Labs develops fast intraoperative histology imaging that delivers real‑time tissue analysis during tumor resections. The platform combines optical imaging of molecular markers with machine‑learning algorithms to automatically detect residual cancer. It is designed to integrate with existing surgical equipment and workflows, helping surgeons achieve more complete tumor removal.
Funding
Funding not disclosed
Founders
Product
Problem
Surgeons often lack immediate, tissue‑level diagnostic information during tumor resections, resulting in incomplete removal and a higher risk of cancer recurrence. Conventional intraoperative histology is time‑consuming and requires separate laboratory workflows, limiting its practical use in the operating room.
Solution
Arago Labs provides a fast intraoperative histology platform that combines high‑resolution optical imaging with machine‑learning‑based tissue classification to deliver histology‑grade information within minutes of tissue exposure. The system attaches to standard surgical imaging equipment and operates within existing operative workflows, eliminating the need for dedicated hardware or extensive staff training. Real‑time visualizations and margin assessments are presented directly to the surgeon, enabling immediate confirmation of complete tumor excision before closure. On‑device processing ensures low latency and maintains patient data privacy, while optional integration with surgical navigation systems offers contextual guidance. By supplying reliable, tissue‑level feedback at the point of care, the platform aims to improve complete resection rates and reduce postoperative tumor recurrence.
Target Audience
The primary customers are oncologic surgeons—including neurosurgeons, orthopedic oncologists, and general tumor surgeons—and the hospitals or ambulatory surgical centers that support their operative workflows.
Features
- High‑resolution optical imaging module tuned to detect tumor‑specific molecular signatures.
- Deep‑learning algorithms that automatically segment and classify tissue types with sub‑millimeter accuracy.
- Real‑time processing pipeline delivering diagnostic results in under five minutes.
- Plug‑and‑play hardware interface compatible with common OR imaging devices (e.g., microscopes, endoscopes).
- Seamless software integration with existing surgical navigation and electronic health record systems via standard APIs.
- On‑device inference engine to ensure low latency and compliance with data‑privacy regulations.
- User interface that overlays margin maps and confidence scores directly onto the surgeon’s view.