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This is a platform to translate computational techniques developed for endoscopic imaging into clinical systems that form part of first-in-man studies.
The core capabilities that are ready to move to this stage are:
- Classification and detection based on deep learning
- Reconstruction of environment geometry
- Spectral inference from RGB, RGB-D, NB or multispectral imaging systems
It will support multiple projects (both funded and in review/planning) and also clinical studies initially in gastroenterology but also leading to general laparoscopy and potentially microsurgery.
A key focus will be on utilising the WEISS infrastructure on ethics, software and regulation to transition research capability into clinical studies with a feedback loop to iterate on requirements, performance, features, data management and algorithm improvement.
The platform will benefit from other platforms, for example in navigation software, by using their software cycle infrastructure but focusing on the computational endoscopy aspects and special needs. It will also link to new imaging capabilities when they are at a translational stage and be able to support new devices with advanced targeting through vision capabilities. Electronics support and the possibility to build lightweight devices for OR deployment will also be useful.
Platform lead:
Professor Danail Stoyanov
