The new EU General Data Protection Regulation: what the radiologist should know

The Promise of Artificial Intelligence-Assisted Point-of-Care Ultrasonography in Perioperative Care

The role of point-of-care ultrasonography in the perioperative setting has expanded rapidly over recent years. Revolutionizing this technology further is integrating artificial intelligence to assist clinicians in optimizing images, identifying anomalies, performing automated measurements and calculations, and facilitating diagnoses. Artificial intelligence can increase point-of-care ultrasonography efficiency and accuracy, making it an even more valuable point-of-care tool. Given this topic's importance and ever-changing landscape, this review discusses the latest trends to serve as an introduction and update in this area.

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.

Teleradiology: good practice guide

Teleradiology is the electronic transmission of radiological images from one location to another with the main purpose of interpreting or consulting a diagnosis and must be subject to codes of conduct agreed upon by professional societies. The content of fourteen teleradiology best practice guidelines is analyzed. Their guiding principles are: the best interest and benefit of the patient, quality and safety standards homologous to the local radiology service, and use as a complement and support of the same. As legal obligations: guaranteeing rights by applying the principle of the patient's country of origin, establishing requirements in international teleradiology and civil liability insurance. Regarding the radiological process: integration with the local service process, guaranteeing the quality of images and reports, access to previous studies and reports and complying with the principles of radioprotection. Regarding professional requirements: compliance with the required registrations, licenses and qualifications, training and qualification of the radiologist and technician, prevention of fraudulent practices, respect for labor standards and remuneration of the radiologist. Subcontracting must be justified, managing the risk of commoditization. Compliance with the system's technical standards.

Addressing modern and practical challenges in machine learning: a survey of online federated and transfer learning

Online federated learning (OFL) and online transfer learning (OTL) are two collaborative paradigms for overcoming modern machine learning challenges such as data silos, streaming data, and data security. This survey explores OFL and OTL throughout their major evolutionary routes to enhance understanding of online federated and transfer learning. Practical aspects of popular datasets and cutting-edge applications for online federated and transfer learning are also highlighted in this work. Furthermore, this survey provides insight into potential future research areas and aims to serve as a resource for professionals developing online federated and transfer learning frameworks.

Communicating with patients in the age of online portals-challenges and opportunities on the horizon for radiologists

The deployment of electronic patient portals increasingly allows patients throughout Europe to consult and share their radiology reports and images securely and timely online. Technical solutions and rules for releasing reports and images on patient portals may differ among institutions, regions and countries, and radiologists should therefore be familiar with the criteria by which reports and images are made available to their patients. Radiologists may also be solicited by patients who wish to discuss complex or critical imaging findings directly with the imaging expert who is responsible for the diagnosis. This emphasises the importance of radiologists’ communication skills as well as appropriate and efficient communication pathways and methods including electronic tools. Radiologists may also have to think about adapting reports as their final product in order to enable both referrers and patients to understand imaging findings. Actionable reports for a medical audience require structured, organ-specific terms and quantitative information, whereas patient-friendly summaries should preferably be based on consumer health language and include explanatory multimedia support or hyperlinks. Owing to the cultural and linguistic diversity in Europe dedicated solutions will require close collaboration between radiologists, patient representatives and software developers; software tools using artificial intelligence and natural language processing could potentially be useful in this context. By engaging actively in the challenges that are associated with increased communication with their patients, radiologists will not only have the opportunity to contribute to patient-centred care, but also to enhance the clinical relevance and the visibility of their profession.

A Two-Stage De-Identification Process for Privacy-Preserving Medical Image Analysis

Identification and re-identification are two major security and privacy threats to medical imaging data. De-identification in DICOM medical data is essential to preserve the privacy of patients' Personally Identifiable Information (PII) and requires a systematic approach. However, there is a lack of sufficient detail regarding the de-identification process of DICOM attributes, for example, what needs to be considered before removing a DICOM attribute. In this paper, we first highlight and review the key challenges in the medical image data de-identification process. In this paper, we develop a two-stage de-identification process for CT scan images available in DICOM file format. In the first stage of the de-identification process, the patient's PII-including name, date of birth, etc., are removed at the hospital facility using the export process available in their Picture Archiving and Communication System (PACS). The second stage employs the proposed DICOM de-identification tool for an exhaustive attribute-level investigation to further de-identify and ensure that all PII has been removed. Finally, we provide a roadmap for future considerations to build a semi-automated or automated tool for the DICOM datasets de-identification.

Smartphone use for tomographic evaluation: application in endodontic diagnosis

INTRODUCTION

Portable equipment that allows quick exchanges of information, such as smartphones, is increasingly important in Dentistry. Thus, they have become frequently used, with the potential to contribute to the tomographic evaluation. This study aimed to evaluate the accuracy of smartphone applications for diagnosing the root canal system (RCS) and measuring the root canal length.

METHODS

DICOM files of 92 lower incisor teeth were evaluated by two trained and calibrated examiners using the CS 3D Imaging software and two smartphone applications (DroidRender and Horos Mobile). The RCS was assessed according to Vertucci's classification, and the tooth length was measured using linear cusp-apex measurements. The diagnostic reference standard was obtained by the mode and the mean of the evaluations made by three experienced examiners using the CS 3D Imaging software. The diagnostic performance of RCS was evaluated using sensitivity (Se), specificity (Sp), and overall accuracy (Ac). The Bland-Altman analysis was used to assess the agreement of linear measurements.

RESULTS

The diagnostic tests showed similar performance between the smartphone applications (DroidRender: Se = 1.00; Sp = 0.95; Ac = 0.97; Horos: Se = 0.95; Sp = 0.94; Ac = 0.95) and the computer software (Se = 0.97 - 0.95; Sp = 0.93 - 0.96; Ac = 0.95 - 0.96). The smartphone applications showed discrepancies greater than 1.0 mm for the dental lengths, which may signal relevant differences in some clinical situations.

CONCLUSION

Smartphone applications offered similar diagnostic performance in comparison to the computer software for the RCS evaluation.

Evaluation of a Novel Teleradiology Technology for Image-Based Distant Consultations: Applications in Neurosurgery

In emergency settings, fast access to medical imaging for diagnostic is pivotal for clinical decision making. Hence, a need has emerged for solutions that allow rapid access to images on small mobile devices (SMD) without local data storage. Our objective was to evaluate access times to full quality anonymized DICOM datasets, comparing standard access through an authorized hospital computer (AHC) to a zero-footprint teleradiology technology (ZTT) used on a personal computer (PC) or SMD using national and international networks at a regional neurosurgical center. Image datasets were sent to a senior neurosurgeon, outside the hospital network using either an AHC and a VPN connection or a ZTT (Image Over Globe (IOG)), on a PC or an SMD. Time to access DICOM images was measured using both solutions. The mean time using AHC and VPN was 250 ± 10 s (median 249 s (233–274)) while the same procedure using IOG took 50 ± 8 s (median 49 s (42–60)) on a PC and 47 ± 20 s (median 39 (33–88)) on a SMD. Similarly, an international consultation was performed requiring 23 ± 5 s (median 21 (16–33)) and 27 ± 1 s (median 27 (25–29)) for PC and SMD respectively. IOG is a secure, rapid and easy to use telemedicine technology facilitating efficient clinical decision making and remote consultations.

An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging

Artificial intelligence (AI) is one of the most promising fields of research in medical imaging so far. By means of specific algorithms, it can be used to help radiologists in their routine workflow. There are several papers that describe AI approaches to solve different problems in liver and pancreatic imaging. These problems may be summarized in four different categories: segmentation, quantification, characterization and image quality improvement. Segmentation is usually the first step of successive elaborations. If done manually, it is a time-consuming process. Therefore, the semi-automatic and automatic creation of a liver or a pancreatic mask may save time for other evaluations, such as quantification of various parameters, from organs volume to their textural features. The alterations of normal liver and pancreas structure may give a clue to the presence of a diffuse or focal pathology. AI can be trained to recognize these alterations and propose a diagnosis, which may then be confirmed or not by radiologists. Finally, AI may be applied in medical image reconstruction in order to increase image quality, decrease dose administration (referring to computed tomography) and reduce scan times. In this article, we report the state of the art of AI applications in these four main categories.

Web-Based GPU-Accelerated Application for Multiplanar Reconstructions from Conventional 2D Ultrasound

PURPOSE

 In ultrasound education there is a need for interactive web-based learning resources. The purpose of this project was to develop a web-based application that enables the generation and exploration of volumetric datasets from cine loops obtained with conventional 2D ultrasound.

MATERIALS AND METHODS

 JavaScript code for ultrasound video loading and the generation of volumetric datasets was created and merged with an existing web-based imaging viewer based on JavaScript and HTML5. The Web Graphics Library was utilized to enable hardware-accelerated image rendering.

RESULTS

 The result is a web application that works in most major browsers without any plug-ins. It allows users to load a conventional 2D ultrasound cine loop which can subsequently be manipulated with on-the-fly multiplanar reconstructions as in a Digital Imaging and Communications in Medicine (DICOM) viewer. The application is freely accessible at (http://www.castlemountain.dk/atlas/index.php?page=mulrecon&mulreconPage=sonoviewer) where a demonstration of web-based sharing of generated cases can also be found.

CONCLUSION

 The developed web-based application is unique in its ability to easily perform loading of one's own ultrasound clips and conduct multiplanar reconstructions where interactive cases can be shared on the Internet.

Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications

The reconstruction of computed tomography (CT) images is an active area of research. Following the rise of deep learning methods, many data-driven models have been proposed in recent years. In this work, we present the results of a data challenge that we organized, bringing together algorithm experts from different institutes to jointly work on quantitative evaluation of several data-driven methods on two large, public datasets during a ten day sprint. We focus on two applications of CT, namely, low-dose CT and sparse-angle CT. This enables us to fairly compare different methods using standardized settings. As a general result, we observe that the deep learning-based methods are able to improve the reconstruction quality metrics in both CT applications while the top performing methods show only minor differences in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). We further discuss a number of other important criteria that should be taken into account when selecting a method, such as the availability of training data, the knowledge of the physical measurement model and the reconstruction speed.

Social Media Use and Community-Based Cardiovascular Health-care Professionals: Perception versus Reality

Social media use has dramatically increased in the past two decades. This growth has been seen in the health-care field as well. Social media is being used for a variety of activities including networking, education, public health, and marketing. Health-care professionals in cardiology participate in social media to varying degrees and in different ways. Current studies have focused primarily on physicians who have an established presence on social media. To learn more about the social media habits of community-based cardiology providers, we queried attendants at a cardiovascular conference held by our health-care system. The purpose of this article is to: Highlight the social media habits of a range of community-based cardiology providers and distinguish between producing and consuming social media. There is a predominance of social media content consumers compared to producersOutline important considerations when assessing the risks and benefits of social media use and the perceived concerns of cardiology health-care professionalsEmphasize the need to incorporate guidelines for social media use into institutional policies and provide training on social media use to the health-care community.

The rise of #SocialMedia in the Nephrology world

Social media (SoMe) use has increased exponentially in the last decade and is having a profound impact on the Nephrology world. The use of these platforms is contributing to continuous educational and professional development by exposing nephrologists to new research, allowing them to connect with experts, to exchange experiences, or to engage in scientific debates. Here, we introduce the basics of SoMe, focusing on Twitter because it is the most popular SoMe platform used by the medical community for professional purposes. We will review the main education platforms and tools available, such as visual abstracts, blogs, tweetorials, videos, and podcasts. We will also focus on their different applications for educational purposes such as online journal clubs, webinars, or online games. The role of SoMe in academic promotion, dissemination of research, expansion of nephrology societies and coverage of scientific events will also be discussed. In the end, we will reflect on SoMe risks and limitations, much-needed developments in SoMe platforms and the challenges ahead of us.

Artificial Intelligence in Radiology-Ethical Considerations

Artificial intelligence (AI) is poised to change much about the way we practice radiology in the near future. The power of AI tools has the potential to offer substantial benefit to patients. Conversely, there are dangers inherent in the deployment of AI in radiology, if this is done without regard to possible ethical risks. Some ethical issues are obvious; others are less easily discerned, and less easily avoided. This paper explains some of the ethical difficulties of which we are presently aware, and some of the measures we may take to protect against misuse of AI.

Understanding Social Media: Opportunities for Cardiovascular Medicine

Cardiology professionals have used social media platforms such as Twitter to gain exposure to new research, network with experts, share opinions, and engage in scientific debates. The power of social media to communicate openly, with wide-reaching access worldwide, and at a rate faster than ever before makes it a formidable force and voice. However, evolving individual and institutional use has resulted in uncertainty for all parties on how to optimally advance this newer digital frontier. Thus, the purpose of this paper is to: 1) introduce the basics of social media usage (with the focus on Twitter); 2) provide perspective on best social media practices in academic and clinical cardiovascular medicine; and 3) present a vision for social media and the future of cardiovascular medicine.

Patient Safety in Medical Imaging: a joint paper of the European Society of Radiology (ESR) and the European Federation of Radiographer Societies (EFRS)

The fundamental professional roles of radiographers and radiologists are focused on providing benefit to patients with our skills, while maintaining their safety at all times. There are numerous patient safety issues in radiology which must be considered. These encompass: protection from direct harm arising from the techniques and technologies we use; ensuring physical and psychological well-being of patients while under our care; maintaining the highest possible quality of service provision; and protecting the staff to ensure they can deliver safe services. This paper summarises the key categories of safety issues in the provision of radiology services, from the joint perspectives of radiographers and radiologists, and provides references for further reading in all major relevant areas.This is a joint statement of the European Society of Radiology (ESR) and the European Federation of Radiographer Societies (EFRS), published simultaneously in Insights into Imaging [DOI:10.1186/s13244-019-0721-y] and Radiography (DOI: 10.1016/j.radi.2019.01.009).

Clinical Imaging Research: Higher Evidence, Global Collaboration, Improved Reporting, and Data Sharing Are the Grand Challenges

The four grand challenges of imaging research—increasing evidence levels, enhancing global collaboration, improving research reporting quality, and sharing trial data—can be addressed, utilizing the tail wind of digital transformation, by consolidating actions of all stakeholders, with the ultimate goal of evidence-based, reproducible, generalizable, and broadly accepted results that will improve the quality and consistency of patient care.

OpenEHR and General Data Protection Regulation: Evaluation of Principles and Requirements

Background

Concerns about privacy and personal data protection resulted in reforms of the existing legislation in the European Union (EU). The General Data Protection Regulation (GDPR) aims to reform the existing directive on the topic of personal data protection of EU citizens with a strong emphasis on more control of the citizens over their data and in the establishment of rules for the processing of personal data. OpenEHR is a standard that embodies many principles of interoperable and secure software for electronic health records (EHRs) and has been advocated as the best approach for the development of hospital information systems.

Objective

This study aimed to understand to what extent the openEHR standard can help in the compliance of EHR systems to the GDPR requirements.

Methods

A list of requirements for an EHR to support GDPR compliance and also a list of the openEHR design principles were made. The requirements were categorized and compared with the principles by experts on openEHR and GDPR.

Results

A total of 50 GDPR requirements and 8 openEHR design principles were identified. The openEHR principles conformed to 30% (15/50) of GDPR requirements. All the openEHR principles were aligned with GDPR requirements.

Conclusions

This study showed that the openEHR principles conform well to GDPR, underlining the common wisdom that truly realizing security and privacy requires it to be built in from the start. By using an openEHR-based EHR, the institutions are closer to becoming compliant with GDPR while safeguarding the medical data.

Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?

Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the "fourth" industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.