Periodic Reporting for period 2 - AICE (AI supported picture analysis in large bowel camera capsule endoscopy)
Okres sprawozdawczy: 2024-03-01 do 2025-08-31
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 during 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.
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 during 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.
AICE aims to enable routine clinical application of a comprehensive diagnostic setup for large bowel investigations by validating an Artificial Intelligence Algorithm (AIA) supported CCE. The project focuses on optimizing diagnostic accuracy, enhancing patient experience, and reducing medical, technical, and economic barriers to transitioning from Optical Colonoscopy (OC) to Colon Capsule Endoscopy (CCE). The anticipated benefits include earlier treatment initiation, lower rates of advanced-stage colorectal cancer (CRC), improved patient compliance, and significant cost reductions.
Key activities involve strengthening the clinical delivery of CCE, including defining the patient populations where CCE outperforms OC, optimizing AIA for fast and reliable image analysis, and validating its diagnostic capabilities internally and externally. These technical advancements are supported by the development of a secure and flexible remote system for data storage and handling, designed to deliver high-quality diagnostic reports directly to healthcare professionals within minutes.
Further efforts have included the establishment of a robust pathway for clinical diagnostics, integrating ethical and patient-centered considerations, alongside preparatory work for commercialization—covering CE marking, business cases, and cost analyses. Additionally, patients and their families have actively contributed feedback through dedicated committees to ensure the solutions align with their preferences and needs.
Looking forward, AICE will finalize European guidelines to promote the clinical integration of AI-supported CCE and generate scalable, sustainable business cases to enable widespread adoption across healthcare systems. These advancements aim to future-proof diagnostic programs, ensuring adaptability to ongoing developments in hardware and software for gastrointestinal diagnostics.
Key activities involve strengthening the clinical delivery of CCE, including defining the patient populations where CCE outperforms OC, optimizing AIA for fast and reliable image analysis, and validating its diagnostic capabilities internally and externally. These technical advancements are supported by the development of a secure and flexible remote system for data storage and handling, designed to deliver high-quality diagnostic reports directly to healthcare professionals within minutes.
Further efforts have included the establishment of a robust pathway for clinical diagnostics, integrating ethical and patient-centered considerations, alongside preparatory work for commercialization—covering CE marking, business cases, and cost analyses. Additionally, patients and their families have actively contributed feedback through dedicated committees to ensure the solutions align with their preferences and needs.
Looking forward, AICE will finalize European guidelines to promote the clinical integration of AI-supported CCE and generate scalable, sustainable business cases to enable widespread adoption across healthcare systems. These advancements aim to future-proof diagnostic programs, ensuring adaptability to ongoing developments in hardware and software for gastrointestinal diagnostics.
AICE aims to transform colorectal diagnostics by integrating Artificial Intelligence (AI) with Colon Capsule Endoscopy (CCE), offering a safer, less invasive, and more efficient alternative to Optical Colonoscopy (OC). CCE demonstrates superior sensitivity in detecting polyps and cancers, enabling earlier diagnoses and improved prevention strategies while minimizing complications and patient discomfort.
Through AI-driven image analysis and pre-test algorithms, AICE optimizes diagnostic precision, reduces variability, and streamlines workflows. Enhanced hardware and software development promise further advancements in efficiency and accuracy, supporting personalized care and tailored diagnostics.
To ensure clinical uptake, AICE addresses cost-efficiency and develops guidelines for implementation, paving the way for sustainable, patient-centered solutions that meet healthcare demands and reduce overall system burden.
Through AI-driven image analysis and pre-test algorithms, AICE optimizes diagnostic precision, reduces variability, and streamlines workflows. Enhanced hardware and software development promise further advancements in efficiency and accuracy, supporting personalized care and tailored diagnostics.
To ensure clinical uptake, AICE addresses cost-efficiency and develops guidelines for implementation, paving the way for sustainable, patient-centered solutions that meet healthcare demands and reduce overall system burden.