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

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.

Assessing the Role of Artificial Intelligence (AI) in Clinical Oncology: Utility of Machine Learning in Radiotherapy Target Volume Delineation

“…The United Kingdom National Health Service (NHS) is actively exploring the evidence for AI and generating a strategy for adapting and application of AI to contribute and optimise patient care [45]. The potential application of AI has been subject of much interest to the radiology and oncology community [46,47]. The United Kingdom Royal College of Radiologists (RCR) has identified AI as one of the most significant technological advancement in healthcare.…”

“…Machine learning is an iterative process of detecting links through computer models and algorithms and requires a huge database of raw data for its learning process. Cancer images, radiology data and patient radiotherapy contours are often stored in secure servers that are not linked in local hospital setups [47]. The huge barrier for advancement of machine learning in radiotherapy would be the logistics of accessing such radiotherapy databases and diagnostic images of sufficient quality for machine learning training.…”

Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance

“…Furthermore, we expect a shift in the knowledge and experience from pure treatment planning (workflow) to an understanding of the working principle of the models and interpretation of the output of the models. Medical physicists involved in AI should familiarize themselves with all relevant aspects, and (future) curricula of the Qualified Medical Physics expert and radiation oncologists should incorporate big data and artificial intelligence [148].…”

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

“…Given their skills in numerical analysis and clinical integration, MPs can significantly aid in the management of aggregate data [4], which will include clinical and image data from multiple modalities, such as PET, CT, radiography MRI, ultrasound, daily CBCT, hybrid imaging, such as PET/CT and PET/MRI, 3D/4D and image time series, and 3D/4D dose distribution from RT. MP will be involved in the development of metrics to assess the quality and completeness of data, methods to curate data, and QA programs of data archives [140].…”