An Artificial Intelligence-Based Federated Learning Platform to Boost Precision Medicine in Rare Hematological Diseases: An Initiative By GenoMed4all and Synthema Consortia

作者：G. Asti, S. D'amico, L. Carota, D. Piscia, Francesco Casadei, N. Merleau, Patricia Alonso de Apellániz, A. Kubasch, S. Gloaguen, Nicole Modler, C. González Martín, C. Rollo, J. Parras, Mattia Delleani, Elena Zazzetti, Elisabetta Sauta, N. Curti, Nicolas Derus, Gianluca Carlini, Daniele Dall’Olio, C. Sala, Lorenzo Dall'olio, L. Lanino, G. Maggioni, Alessia Campagna, Marilena Bicchieri, Arturo Bonometti, Cesare Lancellotti, Daoud Rahal, Luca di Tommaso, T. Sanavia, P. Fariselli, Matteo Zampini, Matteo Brindisi, V. Savevski, U. Platzbecker, M. Díez-Campelo, Lin-Pierre Zhao, Pierre Fenaux, T. Haferlach, Vincent Planat, Raffaella Colombatti, M. Mañú-Pereira, Anders Krogh, Silvia Uribe, S. Zazo, E. Giampieri, G. Castellani, M. D. Della Porta, Federico Alvarez · 发表于：Blood · 年份：2024 · DOI：10.1182/blood-2024-205541 · 被引用次数：5

BACKGROUND. Most oncological and non-oncological hematological conditions fall under the category of rare diseases. Rare diseases present unique challenges due to the limited availability of data, which impacts diagnostic rates and the generation of clinical evidence. Overall, they constitute a public health concern, highlighting the urgent need to develop new methods for improving data accessibility. In this context, Federated Learning (FL) is a Machine Learning approach that allows multiple centers to collaborate on complex research questions without the need to centralize or share data. This project was conducted by the Genomed4all and Synthema consortia with the goal of developing an innovative FL platform for rare hematological diseases. This platform enables the development of novel Artificial Intelligence (AI) models for personalized medicine without data sharing, to be implemented in the referral centers of EuroBloodNET, the European Reference Network for rare hematological diseases. The aims of the project were: 1) to develop robust federated models for personalized prediction using multicentric, real-world datasets; 2) to protect patients' privacy; and 3) to enhance collaboration between institutions while avoiding the creation of centralized data repositories. METHODS. The FL platform includes a manager node (MN) and multiple worker nodes (WN). Users upload their model to the MN, which distributes it to WNs for local training. Trained weights are returned to the ...