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NephroCAGE—German-Canadian Consortium on AI for Improved Kidney Transplantation Outcome: Protocol for an Algorithm Development and Validation Study

Schapranow, Matthieu-P. ; Bayat, Mozhgan; Rasheed, Aadil; Naik, Marcel; Graf, Verena; Schmidt, Danilo; Budde, Klemens; Cardinal, Héloïse; Sapir-Pichhadze, Ruth; Fenninger, Franz; Sherwood, Karen; Keown, Paul; Günther, Oliver P.; Pandl, Konstantin D. ORCID iD icon 1,2; Leiser, Florian ORCID iD icon 1,2; Thiebes, Scott ORCID iD icon 1,2; Sunyaev, Ali 1,2; Niemann, Matthias; Schimanski, Andreas; ... mehr

Abstract (englisch):

Background:
Recent advances in hardware and software enabled the use of artificial intelligence (AI) algorithms for analysis of complex data in a wide range of daily-life use cases. We aim to explore the benefits of applying AI to a specific use case in transplant nephrology: risk prediction for severe posttransplant events. For the first time, we combine multinational real-world transplant data, which require specific legal and technical protection measures.

Objective:
The German-Canadian NephroCAGE consortium aims to develop and evaluate specific processes, software tools, and methods to (1) combine transplant data of more than 8000 cases over the past decades from leading transplant centers in Germany and Canada, (2) implement specific measures to protect sensitive transplant data, and (3) use multinational data as a foundation for developing high-quality prognostic AI models.

Methods:
To protect sensitive transplant data addressing the first and second objectives, we aim to implement a decentralized NephroCAGE federated learning infrastructure upon a private blockchain. Our NephroCAGE federated learning infrastructure enables a switch of paradigms: instead of pooling sensitive data into a central database for analysis, it enables the transfer of clinical prediction models (CPMs) to clinical sites for local data analyses. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000167638
Veröffentlicht am 24.01.2024
Originalveröffentlichung
DOI: 10.2196/48892
Scopus
Zitationen: 1
Dimensions
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2023
Sprache Englisch
Identifikator ISSN: 1929-0748
KITopen-ID: 1000167638
Erschienen in JMIR Research Protocols
Verlag JMIR Publications
Band 12
Seiten Art.-Nr.: e48892
Vorab online veröffentlicht am 22.12.2023
Schlagwörter posttransplant risks; kidney transplantation; federated learning infrastructure; clinical prediction model; donor-recipient matching; multinational transplant data set
Nachgewiesen in Dimensions
Scopus
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