Reengineering workflow for curation of DICOM datasets

Reusable, publicly available data is a pillar of open science and rapid advancement of cancer imaging research. Sharing data from completed research studies not only saves research dollars required to collect data, but also helps insure that studies are both replicable and reproducible. The Cancer Imaging Archive (TCIA) is a global shared repository for imaging data related to cancer. Insuring the consistency, scientific utility, and anonymity of data stored in TCIA is of utmost importance. As the rate of submission to TCIA has been increasing, both in volume and complexity of DICOM objects stored, the process of curation of collections has become a bottleneck in acquisition of data. In order to increase the rate of curation of image sets, improve the quality of the curation, and better track the provenance of changes made to submitted DICOM image sets, a custom set of tools was developed, using novel methods for the analysis of DICOM data sets. These tools are written in the programming language perl, use the open-source database PostgreSQL, make use of the perl DICOM routines in the open-source package Posda, and incorporate DICOM diagnostic tools from other open-source packages, such as dicom3tools. These tools are referred to as the Posda Tools. The Posda Tools are open source and available via git at https://github.com/UAMS-DBMI/PosdaTools . In this paper, we briefly describe the Posda Tools and discuss the novel methods employed by these tools to facilitate rapid analysis of DICOM data, including the following: (1) use a database schema which is more permissive, and differently normalized from traditional DICOM databases; (2) perform integrity checks automatically on a bulk basis; (3) apply revisions to DICOM datasets on an bulk basis, either through a web-based interface or via command line executable perl scripts; (4) all such edits are tracked in a revision tracker and may be rolled back; (5) a UI is provided to inspect the results of such edits, to verify that they are what was intended; (6) identification of DICOM Studies, Series, and SOP instances using nicknames which are persistent and have well-defined scope to make expression of reported DICOM errors easier to manage; and (7) rapidly identify potential duplicate DICOM datasets by pixel data is provided; this can be used, e.g., to identify submission subjects which may relate to the same individual, without identifying the individual

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

The vast amount of data produced by today's medical imaging systems has led medical professionals to turn to novel technologies in order to efficiently handle their data and exploit the rich information present in them. In this context, artificial intelligence (AI) is emerging as one of the most prominent solutions, promising to revolutionise every day clinical practice and medical research. The pillar supporting the development of reliable and robust AI algorithms is the appropriate preparation of the medical images to be used by the AI-driven solutions. Here, we provide a comprehensive guide for the necessary steps to prepare medical images prior to developing or applying AI algorithms. The main steps involved in a typical medical image preparation pipeline include: (i) image acquisition at clinical sites, (ii) image de-identification to remove personal information and protect patient privacy, (iii) data curation to control for image and associated information quality, (iv) image storage, and (v) image annotation. There exists a plethora of open access tools to perform each of the aforementioned tasks and are hereby reviewed. Furthermore, we detail medical image repositories covering different organs and diseases. Such repositories are constantly increasing and enriched with the advent of big data. Lastly, we offer directions for future work in this rapidly evolving field

A formal architecture-centric and model driven approach for the engineering of science gateways

From n-Tier client/server applications, to more complex academic Grids, or even the most recent and promising industrial Clouds, the last decade has witnessed significant developments in distributed computing. In spite of this conceptual heterogeneity, Service-Oriented Architecture (SOA) seems to have emerged as the common and underlying abstraction paradigm, even though different standards and technologies are applied across application domains. Suitable access to data and algorithms resident in SOAs via so-called ‘Science Gateways’ has thus become a pressing need in order to realize the benefits of distributed computing infrastructures.In an attempt to inform service-oriented systems design and developments in Grid-based biomedical research infrastructures, the applicant has consolidated work from three complementary experiences in European projects, which have developed and deployed large-scale production quality infrastructures and more recently Science Gateways to support research in breast cancer, pediatric diseases and neurodegenerative pathologies respectively. In analyzing the requirements from these biomedical applications the applicant was able to elaborate on commonly faced issues in Grid development and deployment, while proposing an adapted and extensible engineering framework. Grids implement a number of protocols, applications, standards and attempt to virtualize and harmonize accesses to them. Most Grid implementations therefore are instantiated as superposed software layers, often resulting in a low quality of services and quality of applications, thus making design and development increasingly complex, and rendering classical software engineering approaches unsuitable for Grid developments.The applicant proposes the application of a formal Model-Driven Engineering (MDE) approach to service-oriented developments, making it possible to define Grid-based architectures and Science Gateways that satisfy quality of service requirements, execution platform and distribution criteria at design time. An novel investigation is thus presented on the applicability of the resulting grid MDE (gMDE) to specific examples and conclusions are drawn on the benefits of this approach and its possible application to other areas, in particular that of Distributed Computing Infrastructures (DCI) interoperability, Science Gateways and Cloud architectures developments

Towards a Reference Architecture with Modular Design for Large-scale Genotyping and Phenotyping Data Analysis: A Case Study with Image Data

With the rapid advancement of computing technologies, various scientific research communities have been extensively using cloud-based software tools or applications. Cloud-based applications allow users to access software applications from web browsers while relieving them from the installation of any software applications in their desktop environment. For example, Galaxy, GenAP, and iPlant Colaborative are popular cloud-based systems for scientific workflow analysis in the domain of plant Genotyping and Phenotyping. These systems are being used for conducting research, devising new techniques, and sharing the computer assisted analysis results among collaborators. Researchers need to integrate their new workflows/pipelines, tools or techniques with the base system over time. Moreover, large scale data need to be processed within the time-line for more effective analysis. Recently, Big Data technologies are emerging for facilitating large scale data processing with commodity hardware. Among the above-mentioned systems, GenAp is utilizing the Big Data technologies for specific cases only. The structure of such a cloud-based system is highly variable and complex in nature. Software architects and developers need to consider totally different properties and challenges during the development and maintenance phases compared to the traditional business/service oriented systems. Recent studies report that software engineers and data engineers confront challenges to develop analytic tools for supporting large scale and heterogeneous data analysis. Unfortunately, less focus has been given by the software researchers to devise a well-defined methodology and frameworks for flexible design of a cloud system for the Genotyping and Phenotyping domain. To that end, more effective design methodologies and frameworks are an urgent need for cloud based Genotyping and Phenotyping analysis system development that also supports large scale data processing. In our thesis, we conduct a few studies in order to devise a stable reference architecture and modularity model for the software developers and data engineers in the domain of Genotyping and Phenotyping. In the first study, we analyze the architectural changes of existing candidate systems to find out the stability issues. Then, we extract architectural patterns of the candidate systems and propose a conceptual reference architectural model. Finally, we present a case study on the modularity of computation-intensive tasks as an extension of the data-centric development. We show that the data-centric modularity model is at the core of the flexible development of a Genotyping and Phenotyping analysis system. Our proposed model and case study with thousands of images provide a useful knowledge-base for software researchers, developers, and data engineers for cloud based Genotyping and Phenotyping analysis system development

3rd EGEE User Forum

We have organized this book in a sequence of chapters, each chapter associated with an application or technical theme introduced by an overview of the contents, and a summary of the main conclusions coming from the Forum for the chapter topic. The first chapter gathers all the plenary session keynote addresses, and following this there is a sequence of chapters covering the application flavoured sessions. These are followed by chapters with the flavour of Computer Science and Grid Technology. The final chapter covers the important number of practical demonstrations and posters exhibited at the Forum. Much of the work presented has a direct link to specific areas of Science, and so we have created a Science Index, presented below. In addition, at the end of this book, we provide a complete list of the institutes and countries involved in the User Forum

Managing healthcare transformation towards P5 medicine (Published in Frontiers in Medicine)

Health and social care systems around the world are facing radical organizational, methodological and technological paradigm changes to meet the requirements for improving quality and safety of care as well as efficiency and efficacy of care processes. In this they’re trying to manage the challenges of ongoing demographic changes towards aging, multi-diseased societies, development of human resources, a health and social services consumerism, medical and biomedical progress, and exploding costs for health-related R&D as well as health services delivery. Furthermore, they intend to achieve sustainability of global health systems by transforming them towards intelligent, adaptive and proactive systems focusing on health and wellness with optimized quality and safety outcomes. The outcome is a transformed health and wellness ecosystem combining the approaches of translational medicine, 5P medicine (personalized, preventive, predictive, participative precision medicine) and digital health towards ubiquitous personalized health services realized independent of time and location. It considers individual health status, conditions, genetic and genomic dispositions in personal social, occupational, environmental and behavioural context, thus turning health and social care from reactive to proactive. This requires the advancement communication and cooperation among the business actors from different domains (disciplines) with different methodologies, terminologies/ontologies, education, skills and experiences from data level (data sharing) to concept/knowledge level (knowledge sharing). The challenge here is the understanding and the formal as well as consistent representation of the world of sciences and practices, i.e. of multidisciplinary and dynamic systems in variable context, for enabling mapping between the different disciplines, methodologies, perspectives, intentions, languages, etc. Based on a framework for dynamically, use-case-specifically and context aware representing multi-domain ecosystems including their development process, systems, models and artefacts can be consistently represented, harmonized and integrated. The response to that problem is the formal representation of health and social care ecosystems through an system-oriented, architecture-centric, ontology-based and policy-driven model and framework, addressing all domains and development process views contributing to the system and context in question. Accordingly, this Research Topic would like to address this change towards 5P medicine. Specifically, areas of interest include, but are not limited: • A multidisciplinary approach to the transformation of health and social systems • Success factors for sustainable P5 ecosystems • AI and robotics in transformed health ecosystems • Transformed health ecosystems challenges for security, privacy and trust • Modelling digital health systems • Ethical challenges of personalized digital health • Knowledge representation and management of transformed health ecosystems Table of Contents: 04 Editorial: Managing healthcare transformation towards P5 medicine Bernd Blobel and Dipak Kalra 06 Transformation of Health and Social Care Systems—An Interdisciplinary Approach Toward a Foundational Architecture Bernd Blobel, Frank Oemig, Pekka Ruotsalainen and Diego M. Lopez 26 Transformed Health Ecosystems—Challenges for Security, Privacy, and Trust Pekka Ruotsalainen and Bernd Blobel 36 Success Factors for Scaling Up the Adoption of Digital Therapeutics Towards the Realization of P5 Medicine Alexandra Prodan, Lucas Deimel, Johannes Ahlqvist, Strahil Birov, Rainer Thiel, Meeri Toivanen, Zoi Kolitsi and Dipak Kalra 49 EU-Funded Telemedicine Projects – Assessment of, and Lessons Learned From, in the Light of the SARS-CoV-2 Pandemic Laura Paleari, Virginia Malini, Gabriella Paoli, Stefano Scillieri, Claudia Bighin, Bernd Blobel and Mauro Giacomini 60 A Review of Artificial Intelligence and Robotics in Transformed Health Ecosystems Kerstin Denecke and Claude R. Baudoin 73 Modeling digital health systems to foster interoperability Frank Oemig and Bernd Blobel 89 Challenges and solutions for transforming health ecosystems in low- and middle-income countries through artificial intelligence Diego M. López, Carolina Rico-Olarte, Bernd Blobel and Carol Hullin 111 Linguistic and ontological challenges of multiple domains contributing to transformed health ecosystems Markus Kreuzthaler, Mathias Brochhausen, Cilia Zayas, Bernd Blobel and Stefan Schulz 126 The ethical challenges of personalized digital health Els Maeckelberghe, Kinga Zdunek, Sara Marceglia, Bobbie Farsides and Michael Rigb

Perspectives on distributed computing : thirty people, four user types, and the distributed computing user experience.

This report summarizes the methodology and results of a user perspectives study conducted by the Community Driven Improvement of Globus Software (CDIGS) project. The purpose of the study was to document the work-related goals and challenges facing today's scientific technology users, to record their perspectives on Globus software and the distributed-computing ecosystem, and to provide recommendations to the Globus community based on the observations. Globus is a set of open source software components intended to provide a framework for collaborative computational science activities. Rather than attempting to characterize all users or potential users of Globus software, our strategy has been to speak in detail with a small group of individuals in the scientific community whose work appears to be the kind that could benefit from Globus software, learn as much as possible about their work goals and the challenges they face, and describe what we found. The result is a set of statements about specific individuals experiences. We do not claim that these are representative of a potential user community, but we do claim to have found commonalities and differences among the interviewees that may be reflected in the user community as a whole. We present these as a series of hypotheses that can be tested by subsequent studies, and we offer recommendations to Globus developers based on the assumption that these hypotheses are representative. Specifically, we conducted interviews with thirty technology users in the scientific community. We included both people who have used Globus software and those who have not. We made a point of including individuals who represent a variety of roles in scientific projects, for example, scientists, software developers, engineers, and infrastructure providers. The following material is included in this report: (1) A summary of the reported work-related goals, significant issues, and points of satisfaction with the use of Globus software; (2) A method for characterizing users according to their technology interactions, and identification of four user types among the interviewees using the method; (3) Four profiles that highlight points of commonality and diversity in each user type; (4) Recommendations for technology developers and future studies; (5) A description of the interview protocol and overall study methodology; (6) An anonymized list of the interviewees; and (7) Interview writeups and summary data. The interview summaries in Section 3 and transcripts in Appendix D illustrate the value of distributed computing software--and Globus in particular--to scientific enterprises. They also document opportunities to make these tools still more useful both to current users and to new communities. We aim our recommendations at developers who intend their software to be used and reused in many applications. (This kind of software is often referred to as 'middleware.') Our two core recommendations are as follows. First, it is essential for middleware developers to understand and explicitly manage the multiple user products in which their software components are used. We must avoid making assumptions about the commonality of these products and, instead, study and account for their diversity. Second, middleware developers should engage in different ways with different kinds of users. Having identified four general user types in Section 4, we provide specific ideas for how to engage them in Section 5