Recipient organisationImperial College LondonSource-published name: Imperial College of Science, Technology and Medicine
Funding£859K
PeriodApr 2025 — Mar 2027
In plain English
AI plain-English summary
Every year, 50,000 people in the UK go under the knife for spinal surgery, and in 4 to 12 percent of those minimally invasive procedures, a surgeon’s instrument nicks a nerve or soft tissue it was not meant to touch. These unintended injuries are becoming more common, and they can leave patients with lasting nerve damage that requires costly revision surgery and longer hospital stays. This project aims to stop those injuries before they happen by giving the surgeon’s tool a kind of electronic eyesight. The team will build an endoscopic instrument that uses diffuse reflectance spectroscopy—a technique that reads how different tissues scatter light—to tell bone from nerve in real time. An artificial intelligence algorithm will classify the tissue at the instrument’s tip and warn the surgeon if it is about to cut something delicate. The device will be tested on cadavers in a simulated operating room, with and without the sensing system, to see if it reduces injury rates. If it works, the tool could make complex spinal procedures safer and easier to adopt, cutting NHS costs from complications and improving recovery for thousands of patients each year.
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Research question Can tissue-sensing endoscopic instruments reduce the risk of unintended tissue injuries during minimally invasive spinal surgery? Background The NHS performs 50,000 spinal surgeries annually. Unintended soft tissue injuries occur in 4-12% of minimally invasive spinal surgeries (MISS) and are becoming more common. If the surgical instrument is inadequately positioned, it can cause injury to delicate soft tissue structures and lead to postoperative complications such as nerve injury. Aim & Objectives Aim: To create an optical sensing endoscopic instrument that reduces the risk of tissue injury. Objectives: (1) Design a system for tissue detection, (2) Develop AI algorithms for tissue differentiation during spinal surgery, (3) Integrate fibre optic bundles into an endoscopy system, (4) Test instrument on cadavers in simulated MISS. Methods Our project will deliver a means of optically detecting soft tissues at the tip of the endoscopic instrument during spinal surgery. We will achieve this by taking advantage of diffuse reflectance spectroscopy (DRS) and Artificial intelligence (AI). DRS uses low-cost detectors to identify tissues by assessing how they scatter light. An AI algorithm will be developed for real-time detection and classification of tissues during spinal surgeries. We will validate our instrument in a paired simulated MISS, one used with, and one without the sensing instrument. Timelines for delivery We plan to have a laboratory-validated instrument, ready for clinical evaluation at the end of 24 months. Anticipated impact and dissemination The work will reduce the cost burden of revision surgeries and longer hospital stays on the NHS. Surgeons who are hesitant to adopt MISS due to its complexity will benefit from the real-time feedback provided by our system. This will act as an additional safety measure and make it easier to perform these procedures. It will also improve the well-being of spinal surgery patients by reducing the risk of postoperative complications. Early discussions with spine device manufacturers will begin (under NDA) to ensure rapid translation of the DRS-enabled endoscopy instrument.
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