TartaglIA is a federated artificial intelligence network designed to enhance clinical and healthcare research in Spain. This initiative is driven and led by GMV and is part of the R&D Missions in Artificial Intelligence program within the Spain Digital 2025 agenda and the National Artificial Intelligence Strategy, funded by the European Union through the Next Generation EU funds. It is made up of a consortium of 15 other public and private entities, including Veratech<\/strong>, and aims to accelerate the use of AI in various medical fields.<\/p>\n\n\n\n One of its main goals is to optimize the training of mathematical models that support clinical decision-making, in order to promote personalized and precision medicine.<\/p>\n\n\n\n Among its most notable applications, TartaglIA focuses on diagnostics in four key areas: Alzheimer's, prostate cancer, diabetes, and complex chronic diseases. It uses advanced AI techniques to guide the acquisition of diagnostic-quality ultrasound medical imaging, as well as to analyze large volumes of clinical data, both structured and unstructured. The federated network allows data-owning entities to collaborate without compromising the security and privacy of their data. Each center has a computing node within the Federated Learning Network. It is the Federated Network that moves the learning models to these nodes where the data is stored, not the other way around, following the procedure shown in the following image:<\/p>\n\n\n\n What is Veratech's role in the TartaglIA project?<\/strong><\/p>\n\n\n\n For artificial intelligence models to be trained effectively and efficiently, the data at each node must be harmonized or standardized according to a common format. This ensures that, even though the data is located in different places, it can be accessed and interpreted consistently and uniformly by the AI models both during the training phase and in clinical use.<\/p>\n\n\n\n If the data at each node does not conform to a common data model, the training process becomes significantly more complicated, as it has to work with various formats, and even more challenging, with different semantics. This can lead to incompatibilities or inconsistencies when analyzing the different datasets, resulting in less accurate or useful outcomes. Therefore, harmonization is crucial for federated learning to function properly and to produce robust models.<\/p>\n\n\n\n Within the framework of the TartaglIA project, Veratech<\/strong>, it is the leader of the work package focused on researching methods and techniques to facilitate the creation of standardized data repositories based on the common data model OMOP (OMOP CDM) from heterogeneous clinical data. Research has been conducted on methods for converting data represented in other Electronic Health Record standards, such as OpenEHR, to OMOP. Furthermore, since there was a desire to load unstructured data (images) into OMOP CDM, research has been carried out on a new extension of OMOP CDM in collaboration with FISABIO to store different imaging modalities and their metadata. This work has been addressed by incorporating two new image tables into the common data model of OMOP. Additionally, it has supported the development of the study and application of health standards such as DICOM and MIDS as intermediate steps for the subsequent loading of these images and their metadata into the Extended OMOP CDM.<\/p>\n\n\n\n Todas estas investigaciones se han puesto en pr\u00e1ctica en los paquetes de trabajo en los que se ha trabajado para armonizar datos cl\u00ednicos de inter\u00e9s de pacientes con c\u00e1ncer de pr\u00f3stata, Alzheimer o retinopat\u00eda diab\u00e9tica. Tambi\u00e9n se ha armonizado metainformaci\u00f3n de im\u00e1genes de resonancia magn\u00e9tica de pr\u00f3stata, de anatom\u00eda patol\u00f3gica de biopsias de pr\u00f3stata, resonancia magn\u00e9tica de cerebro y retinograf\u00edas de ojo.<\/p>\n\n\n\n Thus, in these work packages, Veratech has carried out the harmonization of the various data sources to the common data model (OMOP CDM), which originates from the repositories of the data-providing partners in each case. As mentioned, the data sources are heterogeneous and present differences in structure, granularity, terminology, and semantics. Harmonizing clinical data and images is essential for the subsequent training of models for each use case. Initially, a preliminary research effort was necessary to create normalization guidelines for the standardization of the clinical variables of interest, their units, and their allowable ranges. Nonetheless, the majority of the work has consisted of creating complex ETL (Extract, Transform, Load) processes, especially in data transformation and the understanding and application, for the first time, of the OMOP CDM image extension. Subsequently, the data has been loaded locally into the OMOP CDM at each of the data-providing centers for the different work packages. Finally, the queries that retrieve variables and metadata of interest have been specified according to the AI model to be trained. The result of these queries serves as input to the training processes of the AI models.<\/p>\n\n\n\n The scheme followed in TartaglIA is shown below:<\/p>\n\n\n\n Thus, the TartaglIA project has represented a significant advancement in clinical research through the use of health data standards and artificial intelligence techniques in a federated environment. The creation of decision support tools is crucial for improving the quality and safety of healthcare. Furthermore, TartaglIA has fostered collaboration among various national institutions of different types, driving innovation in health.<\/p>\n\n\n\n At Veratech, we are pleased to have been able to collaborate on this innovative project by contributing our knowledge and experience in researching and harmonizing existing data sources in organizations to the extended OMOP CDM with medical imaging data. Through our work, we have added another success story through the use of OMOP CDM and the federated artificial intelligence network that it enables.<\/p>\n\n\n\n El Proyecto Red Federada de Inteligencia Artificial para acelerar la Investigaci\u00f3n Sanitaria (TSI-100205-2021-17), ha sido financiado por el Ministerio para la Transformaci\u00f3n Digital y de la Funci\u00f3n P\u00fablica, mediante el Programa Misiones de I+D en Inteligencia Artificial 2021, en el marco de la Agenda Espa\u00f1a Digital 2025 y de la Estrategia Nacional de Inteligencia Artificial, a trav\u00e9s del Plan de Recuperaci\u00f3n, Transformaci\u00f3n y Resiliencia.<\/p>\n\n\n\n Este proyecto est\u00e1 financiado por el Plan de Recuperaci\u00f3n, Transformaci\u00f3n y Resiliencia. Financiado por la Uni\u00f3n Europea \u2013 NextGenerationEU. Sin embargo, los puntos de vista y las opiniones expresadas son \u00fanicamente los del autor o autores y no reflejan necesariamente los de la Uni\u00f3n Europea o la Comisi\u00f3n Europea. Ni la Uni\u00f3n Europea ni la Comisi\u00f3n Europea pueden ser consideradas responsables de las mismas.<\/p>\n\n\n\n En Europa y Espa\u00f1a se almacenan grandes cantidades de datos de salud, aunque estos suelen estar dispersos en sistemas aislados, lo que dificulta su uso efectivo en investigaci\u00f3n cl\u00ednica y en el desarrollo de modelos de inteligencia artificial (IA). Para solventar esta situaci\u00f3n, el reto principal es construir un entorno colaborativo y seguro que permita compartir datos de salud de manera conjunta, asegurando que permanezcan en los […]<\/p>\n","protected":false},"author":1,"featured_media":453,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[31],"class_list":["post-452","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-noticias","tag-innovacion"],"yoast_head":"\n![\"\"\/](\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXfxVClB8XALHKotKn54eGsIJ4iFYsyDdRUEAxnvKv2GyRBA5TrkUah_0GTB3IjwYN2WcJWBk_G3OgtbqsgkHR4Xkp9tEZ4eeoA9aDx69A0BYAaiu6kTSE6dXwbLhmLC79SIVWCxxX79uVZ6AXE_p0tJ9FDM?key=geve5Jajf1GOTbQPoR_n6g\")
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