AICE - AI supported picture analysis in large bowel camera capsule Endoscopy

Colon capsule endoscopy (CCE) is a new technology, which has the potential to replace 50-65% of all OCs. CCE is preferred by patients, has a lower complication rate and can be performed out of hospital. CCE holds great potential for both patients and hospitals. However, the diagnostic process of CCE includes a time-consuming manual reading done by trained personnel and is expensive and prone to human error. 

Aim

The goal of the project is 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.

The AICE concept will focus on: 

1. completing development of the required AI algorithms
2. external validation of the algorithms 
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. promoting the integration of AICE solutions into clinical practice via guidelines and upscaling adjustments.

To achieve these goals, AICE will use an unprecedented large and diverse collection of existing patient data from nationwide clinical studies, and will include extensive initiatives in the fields of ethics, communication and patient engagement.

Participants

• Project lead: The Region of Southern Denmark, represented by the Department of Surgery (in Svendborg) supported by CAI-X, Odense University Hospital
• The Maersk Mc-Kinney Moller Institute, SDU
• Sundhed.dk
• Stratos AI
• Lund University
• University of Barcelona
• National and Kapodistrian University of Athens
• The Arctic University of Norway
• Umeå University
• Highland Health Board
• University of Strathclyde
• The Data Lab, University of Edinburgh

Funding

The project is funded by Horizon Europe under grant agreement no. 101057400 (European partners) and UK Research and Innovation (UK partners).