Enhancing Surgical Precision through AI-Driven Hyperspectral Imaging

Project ID: 2531ad1516

(You will need this ID for your application)

Research Theme: Healthcare Technologies

UCL Lead department: Computer Science

Department Website

Lead Supervisor: Sophia Bano

Project Summary:

Background Surgeons often face challenges in distinguishing between different tissue types during operations, such as identifying tumour margins in cancer surgeries or visualizing nerves in maxillofacial procedures to avoid inadvertent damage. HSI offers a non-invasive solution by capturing detailed spectral information from tissues, revealing unique signatures that can differentiate healthy tissue from diseased tissue or critical anatomical structures. However, current HSI technology has limitations: • Existing HSI systems often sacrifice spatial detail to capture extensive spectral information, making it difficult to visualize fine anatomical structures. • The vast amount of data generated requires significant computational power, hindering real-time application in surgery. • Surgeons need intuitive tools to interpret hyperspectral data quickly during operations.

Project Goals This project aims to overcome these challenges through cutting-edge AI research:

  1. Develop algorithms that enhance the spatial resolution of hyperspectral images, allowing for detailed visualization of critical structures.
  2. Create efficient data processing techniques and machine learning models for rapid tissue classification and visualization.
  3. Validate the enhanced HSI system across various surgical domains.
  4. Design intuitive interfaces and visualization tools that seamlessly integrate with existing surgical equipment and workflows.

Why This Project? • Interdisciplinary Collaboration: Work alongside experts in computer science, biomedical engineering, and surgeons. • Real-World Impact: Contribute to technology that can improve surgical precision, reduce complications, and enhance patient outcomes across multiple medical fields. • Advanced Skill Development: Gain expertise in machine learning, image processing, high-performance computing.

Who Should Apply? We are seeking motivated candidates with a strong background in computer science, particularly in areas like machine learning, image processing, and computational modelling. A keen interest in biomedical applications and interdisciplinary research is highly desirable.

Join us in pushing the boundaries of surgical technology and making a significant difference in healthcare through innovative research.