A Comprehensive, Artificial Intelligence, Digital Twin Platform Based on Multimodal Real-World Data Integration for Personalized Medicine in Hematology

作者：S. D'amico, Elisabetta Sauta, G. Asti, Mattia Delleani, Elena Zazzetti, Alessia Campagna, L. Lanino, G. Maggioni, M. Ubezio, G. Todisco, Antonio Russo, C. Tentori, Alessandro Buizza, M. Franchi, Lorenzo Dall'olio, Marilena Bicchieri, Matteo Zampini, Matteo Brindisi, F. Ficara, E. Riva, Denise Ventura, L. Crisafulli, Nicole Pinocchio, Antonio Della Porta, Flavia Jacobs, A. Zambelli, V. Savevski, Armando Santoro, Francesc Solé, U. Platzbecker, Pierre Fenaux, M. Díez-Campelo, G. Garcia-Manero, T. Haferlach, S. Kordasti, G. Castellani, F. Efficace, V. Santini, A. Zeidan, R. Komrokji, M. D. Della Porta · 发表于：Blood · 年份：2024 · DOI：10.1182/blood-2024-209634 · 被引用次数：7

Background. Personalized medicine in hematology requires extensive real-world and comprehensive data, including clinical and genomic information. However, integrating, processing and managing such complex data layers in large populations presents significant challenges. Development of patient-tailored models by Artificial Intelligence (AI), known as Digital Twins (DT) offers a novel approach to precision medicine. DT are virtual representations of patients created from multimodal information that can be used to improve diagnosis, prognosis and treatment outcome, improving clinical decision-making. This project aims to advance research by using AI to develop a DT platform for personalized medicine in hematology, with myelodysplastic syndromes (MDS) as case study. MDS are hematological diseases with high clinical and genomic heterogeneity, presenting a challenging scenario for new technologies implementation Methods. We exploited the Federated Learning platform and technology for Synthetic Data generation developed by the GenoMed4All and Synthema consortia to collect multimodal data from a broad population of MDS patients, in a privacy-complaint manner avoiding data sharing. The multimodal longitudinal data included both structured and unstructured information, encompassing clinical records, genomics, image data, treatments, longitudinal outcomes, and patients reported outcomes. We analyzed 22,080 MDS patients by gathering data using different technological strategies from ret...