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

Objective

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.

Field of science

  • /medical and health sciences/clinical medicine/nephrology/kidney diseases
  • /medical and health sciences/clinical medicine/internal medicine
  • /natural sciences/computer and information sciences/artificial intelligence/machine learning/deep learning

Call for proposal

ERC-2020-COG
See other projects for this call

Funding Scheme

ERC-COG - Consolidator Grant

Host institution

UNIVERSITAETSKLINIKUM AACHEN
Address
Pauwelsstrasse 30
52074 Aachen
Germany
Activity type
Higher or Secondary Education Establishments
EU contribution
€ 1 999 375

Beneficiaries (1)

UNIVERSITAETSKLINIKUM AACHEN
Germany
EU contribution
€ 1 999 375
Address
Pauwelsstrasse 30
52074 Aachen
Activity type
Higher or Secondary Education Establishments