Public Funding for Corporatehealth International UK Ltd

Our aim is to help eradicate bowel cancer and improve treatment for a wide range of gastrointestinal diseases through integrated solutions for prevention, early detection, and diagnosis. CorporateHealth International's (CHI) primary product is a tele-endoscopy managed service: Capsule Endoscopy (CE) to visualise the bowel with a swallowable camera and provide a full evaluation to determine bowel cancer risk, diagnose GI symptoms, and precisely decide on optimal follow-up -- if any. This service can be delivered in any setting, from outpatient to self-administration. Currently, CHI is the single service provider for NHS Scotland in a partnership with the CE manufacturer Medtronic, and only commercial company to offer this managed service across Europe. NHS England is evaluating their approach to deploy CE, potentially offering CHI a broad market entry. Video analysis is a resource-intensive process, relying on expert nurses and doctors that inspect up to 400,000 images for suspicious findings in a 2-stage process taking up to 2 hours for a high-quality report. Artificial Intelligence (AI) holds the promise that this process can be made much more efficient while increasing overall accuracy. CHI is in a unique position to create Machine Learning (ML) data for a Neural Network-based AI solution as well as to have the organizational flexibility to make use of that AI within our operational environment. Since January 2019, CHI has been partnering with multiple companies, academics, and NHS organizations in the Innovate UK-sponsored project AID-GI to develop such an AI system. AID-GI concludes February 28, 2021 with a prototype, ready to be used alongside standard operations. Our project is therefore looking to leverage the prototype to conduct all necessary clinical trials for certification, harden the software to achieve medical device Class IIa status and further improve the breadth and accuracy of the AI system. The outcome should then be an AI-led process that reduces the average video analysis time to under 30 minutes. In turn that reduces resource requirements to make a truly large-scale service possible. What would take 100 weeks to recruit to address the UK market opportunity, can then be done in 10 weeks.

no public description

"This project is aiming to improve the diagnostic accuracy for lower gastrointestinal diseases, especially inflammations, by applying _machine learning_ to aid the analysis of internal images. Currently, the gastrointestinal tract is analysed using markers in the stool or with images taken via ingestible cameras or traditional tube-mounted cameras. While the former is limited to detecting cancers (FIT) or generic inflammation (calprotectin) and therefore rather unspecific, the latter is undertaken by human operators and therefore prone to human errors resulting from fatigue, distraction, variable experience or other cognitive limitations. The volume of images and the volume of patients requiring screening, places unmanageable loads on the operators in terms of effort and quality. This project focuses on Video Capsule Endoscopy -- ingestible 'capsule' cameras which are increasingly likely to become the dominant approach to gastrointestinal imagery, where the capsules capture images as patients go about their daily lives rather than attending a hospital or clinic. Through the project we will test the latest approaches to automated image analysis, quantify benefits to the patient, clinician and NHS -- financially and clinically -- and make recommendations on how to implement the solution. When successful, this approach can remove the 'diagnostic bottleneck' that limits optimal IBD treatments, bowel cancer prevention, early detection and other disease investigations."