AICE · AI supported picture analysis in large bowel camera capsule endoscopy

Millions of Europeans undergo optical colonoscopy (OC) every year as a part of diagnostic procedures for bowel issues such as possible bowel cancer. For citizens and patients OC is associated with discomfort, possible complications and sick-days from work, which affects the acceptability to undergo the procedure. OC also constitutes a heavy burden on European hospital capacities. Colon capsule endoscopy (CCE) is a technology that has the potential to replace a substantial proportion of OCs. Studies have shown that CCE is preferred by patients over OC, has a lower complication rate and can be performed out of hospital, in the home of the patient. Thus, CCE holds great potential for both patients and hospitals. However, the current diagnostic process of CCE includes a time-consuming manual reading of the images captured by the capsule (the so-called ""camera pill"") done by trained medical staff, which is both expensive and prone to human error. For CCE to be a viable alternative to OC in clinical practice, these challenges need to be addressed. Therefore, the goal of the AICE project is to develop and validate a set of artificial intelligence algorithms that can assist in the reading of the CCE images to ensure both high quality diagnostics and save crucial clinical resources. The AICE project aims to create a complete and validated AI-assisted pathway that improves CCE diagnostics making the technology clinically viable for the good of patients, health care systems and society. A number of the partners in AICE have been collaborating for a number of years and have completed development of several AI algorithms (AIA) for CCE diagnostics that are now in need of external clinical validation. More algorithms will be completed and prepared for validation within the first 2 years of the AICE project. The AICE concept will focus on: 1) completing development of the remaining AIAs, 2) external validation of all of the AICE AIAs, 3) creating a clinical support system for data handling, storage and transmission, 4) developing a diagnostic pathway that considers quality, efficiency, patient preferences, ethics and economy 5) promotes the integration of AICE solutions into clinical practice via implementation guidelines and upscaling adjustments. To achieve these goals, AICE will use an unprecedented large and diverse collection of existing patient data from nation-wide clinical studies, and will include extensive initiatives in the fields of ethics, communication and patient engagement. To ensure the right competences are present, AICE brings together clinical researchers, epidemiologists, data scientists, digital health experts, health economists, ethics researchers, SMEs, communication experts and experts in regulatory affairs.""