THE CLINICAL CHALLENGE
Detection depends on visualisation
Colonoscopy provides a view of the mucosa through a two-dimensional endoscopic image of a deformable three-dimensional structure. Areas of the colon may therefore be difficult to orientate and adequately inspect.
Even when lesion-detection AI performs well, it cannot identify pathology in mucosa that has not been adequately visualised.
Even when lesion-detection AI performs well, it can only analyse what is visible within the endoscopic image. It cannot identify pathology in mucosa that has not been adequately visualised. This highlights an important distinction between detecting abnormalities and ensuring adequate mucosal coverage.
Understanding which areas of the mucosa have been seen.
Identifying areas that may not have been adequately inspected.
Providing additional procedural information during colonoscopy.
The Solution
AI beyond lesion detection
Core Scope is developing Colon Mapping to address another part of the colonoscopy procedure: mucosal coverage.
Maps procedural visualisation during colonoscopy.
Aims to identify areas that may not have been adequately inspected.
Designed to complement, rather than replace, existing CADe technologies.
Why It Matters
Knowing What’s Been Seen
The future of colonoscopy is not only about detecting abnormalities within the image. It is also about understanding the extent of mucosal visualisation during the procedure.
Colon Mapping is being developed to provide this additional layer of procedural awareness.
AI DEVELOPMENT
Developing Procedural Intelligence
Developing and annotating AI datasets as Core Scope progresses towards design validation and clinical evaluation.
Developing and annotating datasets to support Colon Mapping and future AI validation.
Tracking procedural visualisation to help identify areas that may not have been adequately inspected.
“Progressing towards validation”
Advancing the technology through verification, validation and future clinical evaluation.
- Dataset Development
Building and annotating datasets for the AI programme.
- Technology Development
Developing software for procedural visualisation.
- Future Evaluation
Progressing towards verification, validation and clinical evaluation.