The SINFONIA project repository for AI-based algorithms and health data

• Published in: Frontiers in public health Vol. 12; p. 1448988
• Main Authors: Fernández-Fabeiro, Jorge, Carballido, Álvaro, Fernández-Fernández, Ángel M., Moldes, Manoel R., Villar, David, Mouriño, Jose C.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 23.10.2024
• Subjects: Algorithms; Artificial Intelligence; artificial intelligence-AI; Brain Neoplasms - diagnostic imaging; Breast Neoplasms - diagnostic imaging; cancer imaging; data repository; Female; health data; Humans; Information Dissemination - methods; Lymphoma - diagnostic imaging; Public Health; Radiation Exposure; radiation risk appraisal; Software; data repository; health data; artificial intelligence-AI; cancer imaging; radiation risk appraisal
• ISSN: 2296-2565; 2296-2565
• DOI: 10.3389/fpubh.2024.1448988

The SINFONIA project’s main objective is to develop novel methodologies and tools that will provide a comprehensive risk appraisal for detrimental effects of radiation exposure on patients, workers, caretakers, and comforters, the public, and the environment during the management of patients suspected or diagnosed with lymphoma, brain tumors, and breast cancers. The project plan defines a series of key objectives to be achieved on the way to the main objective. One of these objectives is to develop and operate a repository to collect, pool, and share data from imaging and non-imaging examinations and radiation therapy sessions, histological results, and demographic information related to individual patients with lymphoma, brain tumors, and breast cancers. This paper presents the final version of that repository, a cloud-based platform for imaging and non-imaging data. It results from the implementation and integration of several software tools and programming frameworks under an evolutive architecture according to the project partners’ needs and the constraints of the General Data Protection Regulation. It provides, among other services, data uploading and downloading, data sharing, file decompression, data searching, DICOM previsualization, and an infrastructure for submitting and running Artificial Intelligence models.