Title: CESCAIL - Capsule Endoscopy delivery at SCale through enhanced AI anaLysis
Funder: Department of Health and Social Care

Abstract: Bowel cancer is a key target for innovation in early detection and diagnosis. Here, we propose coupling a minimally-invasive imaging device with Artificial Intelligence to improve efficiency and accuracy of an investigation. Colon capsule endoscopy (CCE) is a clinically viable and cost-effective alternative to traditional imaging methods of the gastrointestinal tract. CCE provides several benefits to patients, clinicians, and the healthcare system, including adding urgently needed capacity to markedly strained services. Analysing CCE videos, however, is resource intensive, represents a time-consuming workload for clinicians, and slows the roll-out of CCE due to staffing constraints. AI can mitigate these constraints, as shown successfully in an Innovate UK-funded project, where a machine learning algorithm has been developed to create a tool (‘AiSPEED’) that supports the pre-processing of CCE videos to detect polyps. An expert achieves better accuracy in less than 20% of time. To conduct clinical validation, the AI algorithm will be locked and in a clinical trial accuracy for polyp detection as well as time spent compared against the ‘gold standard’ standard care pathway (months 4-16). Results will then support certification (months 1-13) Governed by a predetermined change control plan and using trial data, AiSPEED’s accuracy will be improved by using image sequences for machine learning, mimicking the human expert approach (months 2-15) rendering able to classify more pathology categories (months 10-13). The primary objective of this project will be a highly efficient Software-as-Medical-Device, ready for MDR Class IIa certification and acceptance into the standard care pathway. This could cost-effectively offset the shortfall in colonoscopy supply and reduce the workload of clinicians allowing them to pursue other key activities. Dissemination will be multipronged; through close collaboration with stakeholders, including charities such as BCUK and CRUK, institutes such as NICE as well as patients, clinicians and commissioners.

Lay abstract: This project will provide vital steps on the journey towards eliminating bowel cancer &ndash; one of the few major cancers potentially curable if detected early enough. This makes the disease a key target for innovative early detection and diagnosis. As well as targeting bowel cancer, the approach we are proposing will also improve early detection and diagnosis for a wide range of gastrointestinal diseases. The project will jointly be led by UHCW NHS Trust and CorporateHealth International (CHI). In Scotland, CHI is delivering the world s first national, community-based colon capsule endoscopy (CCE) service for up to 15,000 patients annually. CCE is a swallowable camera the size of a large vitamin pill. It is proving to be a viable, effective alternative to traditional fibre optic cameras for imaging the gastrointestinal tract. It already enables endoscopies to be carried out in the community, rather than at hospitals, and delivers a range of benefits to patients, clinicians and the healthcare system, adding urgently needed capacity to a strained system that already had an 85,000 waiting list before the pandemic intensified the problem. However, accurately analysing CCE videos is a resource intensive exercise that can take trained clinicians 20-120 minutes to assess up to 400,000 images from a max 12-hour video. This not only leads to a time-consuming workload for clinicians better spent directly with patients, but also slows the roll-out of CCE as recruiting and training staff takes time. Artificial Intelligence can reduce that constraint as a current Innovate UK-funded project has successfully shown. There, under CHI leadership and in collaboration with NHS, academic, and industrial partners, machine learning was developed to train a neural network algorithm to do the bulk of the work. When using the prototype AI, the expert was already able to achieve the same accuracy in less than 20% of the time. In this project, we will make prototypes production read, enhance accuracy, and conduct a clinical trial. This will allow the demonstration of diagnostic accuracy and productivity of the AI, achieve Software-as-Medical-Device certification and integrate this into a standard care pathway. World-class experts from NHS University Hospital Coventry & Warwickshire (UHCW) and NHS Highland lead the clinical trial and analysis, renowned scientists from University Barcelona continue to develop machine learning algorithms, and NHS AGEMCSU supports the pathway adaptation. CHI leads and rapidly makes the innovation accessible to patients in the NHS and across the northern hemisphere through its managed service. Patients and clinicians have already played a key role in defining the requirements and aims of the current project through a study by NHS AGEM. They will continue to provide an important voice to design a new pathway that is not just accurate and safe, but highly acceptable.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES;4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;7.3 MANAGEMENT AND DECISION MAKING
Health categories: CANCER AND NEOPLASMS