The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession. (December 2018)

• Record Type: Journal Article
• Authors: Kortesniemi, Mika; Tsapaki, Virginia; Trianni, Annalisa; Russo, Paolo; Maas, Ad; Källman, Hans-Erik; Brambilla, Marco; Damilakis, John
• Is Part Of: Physica medica. Volume 56(2018)
• Page Start: 90
• Page End: 93
• Publication Date: 2018-12
• Subjects: Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2018.11.005
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Artificial intelligence is profoundly changing professions, applications and research. Emerging AI methods may enable more comprehensive optimisation, dosimetry and QA. Challenges in data utilisation involve access, privacy, labeling and validation. Technological transform has also potential impact on our multidisciplinary role. Our professional role and education should keep up with the development of AI methods. Abstract: Big data and deep learning will profoundly change various areas of professions and research in the future. This will also happen in medicine and medical imaging in particular. As medical physicists, we should pursue beyond the concept of technical quality to extend our methodology and competence towards measuring and optimising the diagnostic value in terms of how it is connected to care outcome. Functional implementation of such methodology requires data processing utilities starting from data collection and management and culminating in the data analysis methods. Data quality control and validation are prerequisites for the deep learning application in order to provide reliable further analysis, classification, interpretation, probabilistic and predictive modelling from the vast heterogeneous big data. Challenges in practical data analytics relate to both horizontal and longitudinal analysis aspects. Quantitative aspects of data validation, quality control, physically meaningful measures, parameter connections and system modelling for the future artificial intelligence (AI) methods are positioned firmly in the field of Medical Physics profession. It is our interest to ensure that our professional education, continuous training and competence will follow this significant global development.