In collaboration with strong partners, Katana Labs leverages its images analysis, Deep Learning model development and software engineering expertise to push the limits in AI-enabled precision diagnostics. Together with Refined Laser Systems GmbH, Universitรคtsklinikum Carl Gustav Carus Dresden and Technische Universitรคt Dresden, the German consortium initiated the ๐๐๐๐ ๐ซ๐๐ฌ๐๐๐ซ๐๐ก ๐ฉ๐ซ๐จ๐ฃ๐๐๐ญ to bring real-time, intraoperative histopathology-based tumor detection directly in the operating room.
The joint mission of this groundbreaking research study is to facilitate precise differentiation between healthy and cancerous tissue during surgery, thereby increasing the likelihood for successful tumor removal and reducing the overall time in surgery.
Today, confirming the successful surgical removal of all cancerous tissue often requires the dedicated histopathological analysis of samples in a pathology lab, including sample transport, preparation, staining, and expert review by one or more pathologists. This process takes time and often has to be repeated. During this histopathological analysis, the patient remains under anesthesia and the surgery is paused, limiting immediate decision-making by the responsible surgeon.
๐ ๐๐๐๐ ๐๐ข๐ฆ๐ฌ ๐ญ๐จ ๐๐ก๐๐ง๐ ๐ ๐ญ๐ก๐๐ญ:
Refined Laser Systems GmbH’ label-free SRS microscopy system generates high-resolution molecular images of fresh tissue directly at the point of care in the operation room.
At Katana Labs, we develop the ๐๐ ๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ๐ฌ, ๐๐๐ญ๐ ๐ข๐ง๐๐ซ๐๐ฌ๐ญ๐ซ๐ฎ๐๐ญ๐ฎ๐ซ๐, ๐๐ง๐ ๐ฌ๐จ๐๐ญ๐ฐ๐๐ซ๐ ๐ญ๐จ ๐๐ง๐๐ฅ๐ฒ๐ณ๐ ๐ญ๐ก๐ข๐ฌ ๐ฅ๐๐๐๐ฅ-๐๐ซ๐๐ ๐ข๐ฆ๐๐ ๐ข๐ง๐ ๐๐๐ญ๐ ๐ข๐ง ๐ซ๐๐๐ฅ ๐ญ๐ข๐ฆ๐. We train the AI models to automatically detect tumor regions in distinction to healthy tissue and provide structured, interpretable outputs that support surgeons and pathologists in decision-making during surgery.
๐๐ก๐ ๐ ๐จ๐๐ฅ๐ฌ:
โ Immediate identification of cancerous tissue directly during surgery
โ AI-assisted decision support to inform the surgeon/pathologist with real-time histopathology data
โ Proof-of-principle for developing a scalable framework for broader applications in cancer surgery
The KURE project is funded by the Bundesministerium fรผr Bildung und Forschung under the ๐๐๐๐ง ๐ฉ๐ซ๐จ๐ ๐ซ๐๐ฆ๐ฆ๐, supporting AI innovations in healthcare.
