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

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.Peer reviewe

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare

Enhancing the impact of Artificial Intelligence in Medicine: A joint AIFM-INFN Italian initiative for a dedicated cloud-based computing infrastructure

none13noArtificial 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.openRetico, Alessandra; Avanzo, Michele; Boccali, Tommaso; Bonacorsi, Daniele; Botta, Francesca; Cuttone, Giacomo; Martelli, Barbara; Salomoni, Davide; Spiga, Daniele; Trianni, Annalisa; Stasi, Michele; Iori, Mauro; Talamonti, CinziaRetico, Alessandra; Avanzo, Michele; Boccali, Tommaso; Bonacorsi, Daniele; Botta, Francesca; Cuttone, Giacomo; Martelli, Barbara; Salomoni, Davide; Spiga, Daniele; Trianni, Annalisa; Stasi, Michele; Iori, Mauro; Talamonti, Cinzi

Estimation of patient skin dose in fluoroscopy : summary of a joint report by AAPM TG357 and EFOMP

Background: Physicians use fixed C-arm fluoroscopy equipment with many interventional radiological and cardiological procedures. The associated effective dose to a patient is generally considered low risk, as the benefit-risk ratio is almost certainly highly favorable. However, X-ray-induced skin injuries may occur due to high absorbed patient skin doses from complex fluoroscopically guided interventions (FGI). Suitable action levels for patient-specific follow-up could improve the clinical practice. There is a need for a refined metric regarding follow-up of X-ray-induced patient injuries and the knowledge gap regarding skin dose-related patient information from fluoroscopy devices must be filled. The most useful metric to indicate a risk of erythema, epilation or greater skin injury that also includes actionable information is the peak skin dose, that is, the largest dose to a region of skin. Materials and Methods: The report is based on a comprehensive review of best practices and methods to estimate peak skin dose found in the scientific literature and situates the importance of the Digital Imaging and Communication in Medicine (DICOM) standard detailing pertinent information contained in the Radiation Dose Structured Report (RDSR) and DICOM image headers for FGI devices. Furthermore, the expertise of the task group members and consultants have been used to bridge and discuss different methods and associated available DICOM information for peak skin dose estimation. Results: The report contributes an extensive summary and discussion of the current state of the art in estimating peak skin dose with FGI procedures with regard to methodology and DICOM information. Improvements in skin dose estimation efforts with more refined DICOM information are suggested and discussed. Conclusions: The endeavor of skin dose estimation is greatly aided by the continuing efforts of the scientific medical physics community, the numerous technology enhancements, the dose-controlling features provided by the FGI device manufacturers, and the emergence and greater availability of the DICOM RDSR. Refined and new dosimetry systems continue to evolve and form the infrastructure for further improvements in accuracy. Dose-related content and information systems capable of handling big data are emerging for patient dose monitoring and quality assurance tools for large-scale multihospital enterprises

Intercomparison of Gafchromic™ films, TL detectors and TL foils for the measurements of skin dose in interventional radiology

Several passive solid state dosemeters, such as GafchromicTM films and thermoluminesscence (TL) detectors, are used to estimate and monitor patient skin doses in interventional radiology. To determine the suitability of XR-TypeR GafchromicTM films and of detectors based on TL materials: pellets, chips and foils to measure skin dose, an intercomparison exercise has been organized within European Dosimetry Radiation Group - Working Group 12 “European Medical ALARA Network” (EURADOS WG12). To test response detectors were exposed to X-ray beams of energies and qualities applied clinically. A blind test was also performed to investigate the accuracy of the dose estimate by detectors exposed to unknown doses. We found the response of films to be strongly dependent on beam quality and filtration (increasing by up to 80 % with respect to reference beam quality). The response of TL detectors was found to be less dependent on beam quality (less than 25% variation), with TL foils showing less than 10% variation with respect to reference beam quality. To accurately estimate patient skin doses in interventional radiology it is important to choose the quality of the calibration beam to be as close as possible to the quality of beams actually applied in clinical work

Radiation dose from medical imaging in end stage renal disease patients: a Nationwide Italian Survey

Background and objectives: End stage renal disease (ESRD) patients are exposed to the risk of ionizing radiation during repeated imaging studies. The variability in diagnostic imaging policies and the accompanying radiation doses across various renal units is still unknown. We studied this variability at the centre level and quantified the associated radiation doses at the patient level. Methods: Fourteen Italian nephrology departments enrolled 739 patients on haemodialysis and 486 kidney transplant patients. The details of the radiological procedures performed over one year were recorded. The effective doses and organ doses of radiation were estimated for each patient using standardized methods to convert exposure parameters into effective and organ doses RESULTS: Computed tomography (CT) was the major contributor (> 77%) to ionizing radiation exposure. Among the haemodialysis and kidney transplant patients, 15% and 6% were in the high ( 65 20 mSv per year) radiation dose groups, respectively. In haemodialysis patients, the most exposed organs were the liver (16 mSv), the kidney (15 mSv) and the stomach (14 mSv), while the uterus (6.2 mSv), the lung (5.7 mSv) and the liver (5.5 mSv) were the most exposed in kidney transplant patients. The average cumulative effective dose (CED) of ionizing radiation among centres in this study was highly variable both in haemodialysis (from 6.4 to 18.8 mSv per patient-year; p = 0.018) and even more so in kidney transplant (from 0.6 to 13.7 mSv per patient-year; p = 0.002) patients. Conclusions: Radiation exposure attributable to medical imaging is high in distinct subgroups of haemodialysis and transplant patients. Furthermore, there is high inter-centre variability in radiation exposure, suggesting that nephrology units have substantially different clinical policies for the application of diagnostic imaging studies