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AI supported picture analysis in large bowel camera capsule endoscopy

Descrizione del progetto

Algoritmi di intelligenza artificiale per far progredire le nuove procedure endoscopiche a bassa invasività

La colonscopia con capsula è una nuova tecnologia che potrebbe sostituire la maggior parte delle attuali procedure di colonscopia ottica, caratterizzate da disagio e complicazioni. Questa tecnica presenta un tasso di complicazioni inferiore e non richiede un ambiente ospedaliero, ma prevede una lettura manuale piuttosto lunga ed è soggetta a errori umani. Il progetto AICE, finanziato dall’UE, si propone di creare un percorso supportato dall’intelligenza artificiale per la diagnostica della colonscopia con capsula, rendendola clinicamente praticabile. Il progetto AICE utilizzerà una raccolta diversificata di dati esistenti sui pazienti per completare e convalidare gli algoritmi di IA per la diagnostica della colonscopia con capsula, creare un sistema di supporto clinico per la gestione, l’archiviazione e la trasmissione dei dati e promuovere l’integrazione della soluzione AICE nella pratica clinica.

Obiettivo

Millions of Europeans undergo optical colonoscopy (OC) every year. OC may be associated with discomfort, complications and sick-days, which affect acceptability, and constitutes a heavy burden on European hospital capacities. 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. For CCE to be a viable alternative to OC these challenges need to be addressed. Thus, our goal 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. We have already completed development of several AI algorithms (AIA) for CCE diagnostics, and more will be completed within 1 ?2 years. The AICE concept will focus on: 1) completing development of the remaining AIAs, 2) external validation the all 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 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.

Coordinatore

REGION SYDDANMARK
Contribution nette de l'UE
€ 1 508 056,00
Indirizzo
DAMHAVEN 12
7100 Vejle
Danimarca

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Regione
Danmark Syddanmark Sydjylland
Tipo di attività
Public bodies (excluding Research Organisations and Secondary or Higher Education Establishments)
Collegamenti
Costo totale
€ 1 508 056,25

Partecipanti (8)

Partner (3)