SOURCE-LINKED INTELLIGENCE

AI supported picture analysis in large bowel camera capsule endoscopy

CORDIS · observation · Publication date unknown

rone 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 tra

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recordType

award

status

SIGNED

region

EU

value

4650794.75

unit

EUR