Large-Scale Graph Networks and AI Applied to Medical Image Data Processing

With the increasing amount of available medical data, computing power and network speed, modern medical imaging is facing an unprecedented amount of data to analyze and interpret. Phenomena such as Big Data-omics stemming from several diagnostic procedures and novel multi-parametric imaging modalities tend to produce almost unmanageable quantities of data. The paper addresses the aforementioned context by assuming that a novel paradigm in massive data processing and automation becomes necessary in order to improve diagnostics and facilitate personalized and precision medicine for each patient. Traditional machine learning concepts have demonstrated many shortcomings when it comes to correctly diagnose fatal diseases. At the same time static graph networks are unable to capture the fluctuations in brain processing and monitor disease evolution. Therefore, artificial intelligence and deep learning are increasingly applied in oncologic medical imaging because they excel at providing quantitative assessments of biomedical imaging characteristics. On the other hand, novel concepts borrowed from modern control have paved the path for a dynamic graph theory that can predict neurodegenerative disease evolution and replace longitudinal studies. We chose two important topics, brain data processing and oncologic imaging to show the relevance of these concepts. We believe that these novel paradigms will impact multiple facets of radiology but are convinced that it is unlikely that they will replace radiologists any time in the near future since there are still many challenges in the clinical implementation.