Imagine a smart camera that can tell the difference between a healthy organ and a tumor during surgery by watching how blood flows through the tissue. It uses a special glowing dye and AI to highlight the exact boundaries of cancer in real-time. This helps surgeons remove the bad tissue precisely without guessing or relying on slow lab tests.
Current methods for characterizing large rectal polyps are unreliable, with biopsies failing in up to 59% of cases and radiology overestimating 50% of lesions. This leads to incorrect surgical choices and unnecessary repeat operations.
An AI-based decision support software that analyzes near-infrared video of ICG-dyed tissue to identify cancer in real-time during surgery.
If you are a device manufacturer dealing with the lack of real-time tissue characterization—this project developed an AI-based decision support tool that identifies cancer distribution in the rectum. This allows for the creation of high-value, AI-integrated endoscopic hardware.
If you are a software firm dealing with the difficulty of validating AI in clinical settings—this project developed a validated classification software for 500 patients. This provides a blueprint for scaling real-time surgical AI tools.
If you are a clinic operator dealing with unreliable biopsies that fail in 46%-59% of large polyp cases—this project developed a real-time AI tool to optimize resection. This reduces the need for repeat surgeries and improves patient outcomes.
Based on available project data, there is no specific pricing or cost per unit mentioned; the project focuses on clinical validation and regulatory research.
The project specifically targets colorectal cancer and rectal polyps, but it uses a general method of analyzing fluorescence dynamics and blood perfusion which could potentially be adapted for other tumors.
Based on available project data, the consortium includes 13 partners, but specific licensing terms or IP ownership agreements are not detailed in the summary.
The project includes dedicated work packages on legal, regulatory, and liability research to address obstacles to the widespread use of AI in the operating room.
The project period runs from 2022-05-01 to 2026-04-30, indicating a four-year window for development and clinical validation.
The consortium is well-balanced for commercialization, featuring 13 partners across 9 countries. With a 23% industry ratio (3 industrial partners, including 2 SMEs), there is a clear bridge between the 5 universities and 3 research institutes. The inclusion of IRCAD for training and EAES for professional guidelines suggests a strong strategy for market adoption and clinical integration.
Contact University College Dublin (UCD) regarding the CLASSICA project coordination.
Contact us to explore licensing opportunities for the CLASSICA AI surgical tool.
