Title: FORE AI
Funder: Department of Health and Social Care

Abstract: Background Colonoscopy could be significantly improved if all doctors could interpret images/video as well as the best doctors. CADDIE is an AI system that uses deep learning to analyse images and supports the doctor to make better decision in detecting and diagnosing cancerous/pre-cancerous polyps. Aims and Objectives Generate high quality clinical and healthcare economic evidence, through an existing multi-centre randomize control trial, that will enable CADDIE to be in a position where it can be adopted and used within the NHS. Project plan and methods used The CADDIE system will be deployed at 10 hospitals to collect video and double read histopathology data from 1000 patients. The results from the AI will be compared to the histopathology. The primary endpoint for the study is the AI characterisation/diagnostic performance. It will be evaluated using the Preservation and Incorporation of Valuable endoscopic Innovations (PIVI) guidelines [2]. The secondary endpoint of the study is the performance of the polyp detection technology that will be retrospectively evaluated. Demographic data will be analysed to identify bias in the AI and factors that may improve performance. Timelines for delivery The trial has 3 phases. Pre trial (M1-6) &ndash; Develop software to support trial data capture and analysis, retrain AI. Healthcare economics scoping. Prepare trial documents. Trial phase (M7-33) &ndash; continuous data collection, quality monitoring and evaluation. Interim analysis of performance. Build healthcare economics model. Quality/Regulatory maintenance. End of Trial (M34-36) &ndash; trial shut down and final analysis of the performance of the AI. Healthcare economics report. Anticipated Impact and Dissemination Impact: High quality clinical data demonstrating CADDIE meets the diagnostic/characterization threshold for incorporation into clinical practice. This will support regulatory technical file and application to NCIE along with the healthcare economic case. Dissemination: Results published in high impact journals and at patient events.

Lay abstract: In the UK, there are 42,000 new cases of colorectal cancer (CRC) and 16,000 deaths each year making it the second leading cause of cancer deaths. Cases of CRC related deaths are forecast to increase by 51% by 2035. This is caused by an aging population and an increase in the number of younger people getting the disease. A 2017 study showed that adults born in the 1990 s are 4 times more likely to get rectal cancer and twice as likely to get colon cancer compared to adults born in the 1950s. Colonoscopy is an important procedure for finding and removing pre-cancerous/cancerous polyps (abnormal tissue growths). However, colonoscopy is not always effective. Studies have shown that doctor/nurse miss up to 25% of polyps. Higher miss rates lead to increased likelihood of cancer. Polyps that are found, are removed and sent away to be tested for cancer. This adds on average 3 weeks to patient waiting times, it is inefficient and causes the patient anxiety. Odin Medical has developed a Computer Assisted Detection and Diagnosis system for colonoscopy. The system acts like a second pair of eyes during the procedure using artificial intelligence (AI) to find/analyse polyps in colonoscopy images. This technology can support doctors to find more polyps and make instant diagnosis leading to better early detection of cancer. This is strategically aligned to the NHS long term plan of saving 55,000 more lives a year by diagnosing more cancers earlier. Our innovative AI solution works with existing hospital equipment. It sends the colonoscopy image from the hospital to a secure cloud/super-computer where they are analysed. The analysis is instantly sent back to the hospital where the doctor can use it to support clinical decision. This makes it cost effective and accessible for all hospitals, small and large. It ensures we are developing AI in accordance with the NHS constitution s first principal: a comprehensive service, available to all. The aim of this project is to demonstrate the benefits of using AI in colonoscopy through a multi-centre randomised controlled trial. These benefits include, better patient outcomes by improving polyp/cancer detection rates, improved patient experience with instant diagnosis and increased operational efficiency. The project will gather clinical data and perform health economic analyses The project has three phases: 1) pre-trial, set up the trial and develop software to collect data 2) trial, perform procedures, collect data and review performance and 3) post-trial, analyse the clinical evidence and build the healthcare economic case for cost savings. A public and patient group has been involved throughout the technical development of the AI system and will continue to be involved. Outcomes: Clinical evidence of clinical safety and efficacy. Healthcare economic case to demonstrate cost savings.

Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING;4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: ORAL AND GASTROINTESTINAL