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AI-augmented, Multiscale Image-based Diagnostics of Chronic Kidney Disease

Descrizione del progetto

Diagnostica avanzata basata su immagini per pazienti con malattia renale cronica

La malattia renale cronica (CKD, Chronic Kidney Disease), che colpisce il 10 % della popolazione mondiale, rappresenta una delle principali cause di morte e un grave onere sociale e sanitario. A causa della mancanza di approcci riproducibili che riflettano specificamente i processi patologici intrarenali e l’attività della malattia, i pazienti con malattia renale cronica beneficiano delle sperimentazioni cliniche randomizzate meno traslazionali e di opzioni di trattamento limitate. Il progetto AIM.imaging.CKD finanziato dall’UE, svilupperà, convaliderà e integrerà la diagnostica basata su immagini per la malattia renale cronica. L’integrazione di competenze interdisciplinari consentirà di stabilire un metodo multiscala dalla diagnostica nano-morfologica a quella micro-morfologica, macro-morfologica e molecolare. Il progetto svilupperà una diagnostica aumentata della biopsia renale ultrastrutturale e istologica a spettro completo, basata sull’intelligenza artificiale, principalmente sull’apprendimento automatico e profondo.

Obiettivo

Chronic kidney disease (CKD) is a major global health problem, affecting 10% of the world population and projected to be the fifth major cause of death in 2040. CKD patients are one of the most complex and multi-morbid populations in internal medicine while at the same time having the least translational randomized clinical trials and limited treatment options. One of the major reasons for this is the lack of reproducible approaches specifically reflecting intrarenal pathological processes and disease activity. The overall goal of AIM.imaging.CKD is to specifically address this unmet need by developing, validating and integrating image-based diagnostics for CKD. The integration of broad interdisciplinary expertise will enable to develop a multiscale approach from nano- to micro- to macromorphological and molecular diagnostics. Specifically, the project will develop augmented full-spectrum ultrastructural (“nano”) and histological (“micro”) renal biopsy diagnostics, focusing on reproducible, quantitative nephropathological analyses and prediction of clinically relevant outcome parameters. The project will also explore macro-morphological and molecular imaging in CKD, focusing on translatable non-invasive approaches. The central feature will be the development of advanced, scalable and modular image analyses models utilizing artificial intelligence (AI), particularly machine and deep learning. Using preclinical testing and clinical validation, the main emphasis will be on accelerated or, whenever possible, direct implementation into the clinical practice. The integration of the above-mentioned tools and technologies provides a comprehensive multiscale and multiplex approach for improved diagnostics of CKD patients and facilitate future randomized clinical trials. At each level, and even more so when integrated, the results are expected to augment and transform image-based diagnostics of kidney diseases, and thereby lead to improved patient management and outcome.

Meccanismo di finanziamento

ERC-COG - Consolidator Grant

Istituzione ospitante

UNIVERSITAETSKLINIKUM AACHEN
Contribution nette de l'UE
€ 1 999 375,00
Indirizzo
Pauwelsstrasse 30
52074 Aachen
Germania

Mostra sulla mappa

Regione
Nordrhein-Westfalen Köln Städteregion Aachen
Tipo di attività
Higher or Secondary Education Establishments
Collegamenti
Costo totale
€ 1 999 375,00

Beneficiari (1)