Retico Alessandra, Avanzo Michele, Boccali Tommaso, Bonacorsi Daniele, Botta Francesca, Cuttone Giacomo, Martelli Barbara, Salomoni Davide, Spiga Daniele, Trianni Annalisa, Stasi Michele, Iori Mauro, Talamonti Cinzia
National Institute for Nuclear Physics (INFN), Pisa Division, 56127 Pisa, Italy.
Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy.
Phys Med. 2021 Nov;91:140-150. doi: 10.1016/j.ejmp.2021.10.005. Epub 2021 Nov 18.
Artificial Intelligence (AI) techniques have been implemented in the field of Medical Imaging for more than forty years. Medical Physicists, Clinicians and Computer Scientists have been collaborating since the beginning to realize software solutions to enhance the informative content of medical images, including AI-based support systems for image interpretation. Despite the recent massive progress in this field due to the current emphasis on Radiomics, Machine Learning and Deep Learning, there are still some barriers to overcome before these tools are fully integrated into the clinical workflows to finally enable a precision medicine approach to patients' care. Nowadays, as Medical Imaging has entered the Big Data era, innovative solutions to efficiently deal with huge amounts of data and to exploit large and distributed computing resources are urgently needed. In the framework of a collaboration agreement between the Italian Association of Medical Physicists (AIFM) and the National Institute for Nuclear Physics (INFN), we propose a model of an intensive computing infrastructure, especially suited for training AI models, equipped with secure storage systems, compliant with data protection regulation, which will accelerate the development and extensive validation of AI-based solutions in the Medical Imaging field of research. This solution can be developed and made operational by Physicists and Computer Scientists working on complementary fields of research in Physics, such as High Energy Physics and Medical Physics, who have all the necessary skills to tailor the AI-technology to the needs of the Medical Imaging community and to shorten the pathway towards the clinical applicability of AI-based decision support systems.
人工智能(AI)技术在医学成像领域的应用已有四十多年。从一开始,医学物理学家、临床医生和计算机科学家就一直在合作,以实现软件解决方案,来增强医学图像的信息含量,包括用于图像解读的基于AI的支持系统。尽管由于当前对放射组学、机器学习和深度学习的重视,该领域最近取得了巨大进展,但在这些工具完全融入临床工作流程以最终实现精准医疗方法来照顾患者之前,仍有一些障碍需要克服。如今,随着医学成像进入大数据时代,迫切需要创新的解决方案来有效处理大量数据并利用大型和分布式计算资源。在意大利医学物理学家协会(AIFM)和国家核物理研究所(INFN)之间的合作协议框架内,我们提出了一种密集计算基础设施模型,特别适合训练AI模型,配备符合数据保护法规的安全存储系统,这将加速医学成像研究领域基于AI的解决方案的开发和广泛验证。该解决方案可以由从事物理互补研究领域(如高能物理和医学物理)的物理学家和计算机科学家开发并投入使用,他们具备将AI技术定制以满足医学成像社区需求的所有必要技能,并缩短基于AI的决策支持系统临床应用的路径。