57 research outputs found

    Structured reporting in cardiovascular computed tomography

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    While investigation techniques and image modalities become more and more advanced, radiology reports have remained in their classic form for the past decades. Structured reporting has shown its potential to increase the clarity, correctness, confidence, concision, completeness, consistency, communication, consultation and standardization of radiology reports. The increased report quality can mostly be attributed to a complete checklist like approach, standardized vocabulary through RadLex and RSNA provided templates which can be adapted to address very specific inquiries. Especially the interdisciplinary approach necessary to design and adapt those templates can ensure that all therapy influencing criteria are evaluated in the report. This may lead to a different therapy and outcome. Structured reporting also harbors great teaching opportunities, such as a checklist-like approach for young radiology residents and an image database of pathological findings. With a large analyzable database of reports, a statistical analysis becomes possible, which can e.g. lead to increasingly better screening algorithms. Technological challenges however, different data formats, varying degrees of quality of structured reporting systems and the concerns about work flow efficiency and report rigidity remain difficulties of structured reporting itself. Despite of this it also provides many future possibilities such as the implementation of medical guide lines into the report format, multi media reports, evaluation of radiation dose, management of follow-up appointments, automatic invoice and reimbursement systems and the improvement of data mining. Given the potential of structured reporting and its impact on patient care, we decided to evaluate its so far unknown benefit for patients with acute PE and PAD. For patients with APE, the structured reports were evaluated by two pulmonologists and two general internists and compared to the reports from the clinical routine of the same patient group. While all four referring clinicians perceived the structured CTPA reports as superior in clarity, only the pulmonologists found additional benefit in content and clinical utility. The structured reports did not alter patients’ management in patients with acute PE significantly. In the study concerning patients with diagnosed or suspected PAD the structured reports (run-off CTA/ lower extremities) were evaluated by two vascular surgeons and two vascular medicine specialists. The results showed, both groups regarded structured reports as superior in clarity, completeness, clinical relevance and usefulness. Especially vascular medicine specialists seemed to appreciate the structured reporting format. As in our PE study, structured reporting did not seem to alter further testing or therapy for the patients included in our study. Both studies demonstrate that referring clinicians prefer structured reporting of cardiovascular CT examinations over conventional reports

    A Multimodal Knowledge-Based Deep Learning Approach for MGMT Promoter Methylation Identification

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    Glioblastoma Multiforme (GBM) is considered one of the most aggressive malignant tumors, characterized by a tremendously low survival rate. Despite alkylating chemotherapy being typically adopted to fight this tumor, it is known that O(6)-methylguanine-DNA methyltransferase (MGMT) enzyme repair abilities can antagonize the cytotoxic effects of alkylating agents, strongly limiting tumor cell destruction. However, it has been observed that MGMT promoter regions may be subject to methylation, a biological process preventing MGMT enzymes from removing the alkyl agents. As a consequence, the presence of the methylation process in GBM patients can be considered a predictive biomarker of response to therapy and a prognosis factor. Unfortunately, identifying signs of methylation is a non-trivial matter, often requiring expensive, time-consuming, and invasive procedures. In this work, we propose to face MGMT promoter methylation identification analyzing Magnetic Resonance Imaging (MRI) data using a Deep Learning (DL) based approach. In particular, we propose a Convolutional Neural Network (CNN) operating on suspicious regions on the FLAIR series, pre-selected through an unsupervised Knowledge-Based filter leveraging both FLAIR and T1-weighted series. The experiments, run on two different publicly available datasets, show that the proposed approach can obtain results comparable to (and in some cases better than) the considered competitor approach while consisting of less than 0.29% of its parameters. Finally, we perform an eXplainable AI (XAI) analysis to take a little step further toward the clinical usability of a DL-based approach for MGMT promoter detection in brain MRI

    Improving knowledge management through the support of image examination and data annotation using DICOM structured reporting

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    [EN] An important effort has been invested on improving the image diagnosis process in different medical areas using information technologies. The field of medical imaging involves two main data types: medical imaging and reports. Developments based on the DICOM standard have demonstrated to be a convenient and widespread solution among the medical community. The main objective of this work is to design a Web application prototype that will be able to improve diagnosis and follow-on of breast cancer patients. It is based on TRENCADIS middleware, which provides a knowledge-oriented storage model composed by federated repositories of DICOM image studies and DICOM-SR medical reports. The full structure and contents of the diagnosis reports are used as metadata for indexing images. The TRENCADIS infrastructure takes full advantage of Grid technologies by deploying multi-resource grid services that enable multiple views (reports schemes) of the knowledge database. The paper presents a real deployment of such Web application prototype in the Dr. Peset Hospital providing radiologists with a tool to create, store and search diagnostic reports based on breast cancer explorations (mammography, magnetic resonance, ultrasound, pre-surgery biopsy and post-surgery biopsy), improving support for diagnostics decisions. A technical details for use cases (outlining enhanced multi-resource grid services communication and processing steps) and interactions between actors and the deployed prototype are described. As a result, information is more structured, the logic is clearer, network messages have been reduced and, in general, the system is more resistant to failures.The authors wish to thank the financial support received from The Spanish Ministry of Education and Science to develop the project "CodeCloud", with reference TIN2010-17804.Salavert Torres, J.; Segrelles Quilis, JD.; Blanquer Espert, I.; Hernández García, V. (2012). Improving knowledge management through the support of image examination and data annotation using DICOM structured reporting. Journal of Biomedical Informatics. 45(6):1066-1074. https://doi.org/10.1016/j.jbi.2012.07.004S1066107445

    Development and Application of a Web-Based Platform for Assessment of Observer Performance in Medical Imaging

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    An Autoethnographic Account of Innovation at the US Department of Veterans Affairs

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    The history of the U.S. Department of Veterans Affairs (VA) health information technology (HIT) has been characterized by both enormous successes and catastrophic failures. While the VA was once hailed as the way to the future of twenty-first-century health care, many programs have been mismanaged, delayed, or flawed, resulting in the waste of hundreds of millions of taxpayer dollars. Since 2015 the U.S. Government Accountability Office (GAO) has designated HIT at the VA as being susceptible to waste, fraud, and mismanagement. The timely central research question I ask in this study is, can healthcare IT at the VA be healed? To address this question, I investigate a HIT case study at the VA Center of Innovation (VACI), originally designed to be the flagship initiative of the open government transformation at the VA. The Open Source Electronic Health Record Alliance (OSEHRA) was designed to promote the open innovation ecosystem public-private-academic partnership. Based on my fifteen years of experience at the VA, I use an autoethnographic methodology to make a significant value-added contribution to understanding and modeling the VA’s approach to innovation. I use several theoretical information system framework models including People, Process, and Technology (PPT), Technology, Organization and Environment (TOE), and Technology Adaptive Model (TAM) and propose a new adaptive theory to understand the inability of VA HIT to innovate. From the perspective of people and culture, I study retaliation against whistleblowers, organization behavioral integrity, and lack of transparency in communications. I examine the VA processes, including the different software development methodologies used, the development and operations process (DevOps) of an open-source application developed at VACI, the Radiology Protocol Tool Recorder (RAPTOR), a Veterans Health Information Systems and Technology Architecture (VistA) radiology workflow module. I find that the VA has chosen to migrate away from inhouse application software and buy commercial software. The impact of these People, Process, and Technology findings are representative of larger systemic failings and are appropriate examples to illustrate systemic issues associated with IT innovation at the VA. This autoethnographic account builds on first-hand project experience and literature-based insights

    Sistema web para optimização do workflow em serviço radiologia

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    Mestrado em Engenharia de Computadores e TelemáticaA ampla adoção de imagens médicas em formato digital nos diversos tipos de instituições de saúde, levantou novos problemas ao nível da gestão de dados e processos. A normalização destes cenários tem sido alvo de atenção nas últimas décadas, esforço que resultou no desenvolvimento e dinamização de normas como DICOM e HL7. Atualmente coexistem dois tipos de sistemas de informação num laboratório de imagem médica que devem funcionar de forma integrada, os RIS que são responsáveis pela gestão das tarefas administrativas e os PACS que fazem a gestão das imagens e informação associada. Esta dissertação teve como objetivo desenhar e implementar uma solução RIS baseada em ferramentas de utilização livre ou código aberto. Assim, começamos por estudar detalhadamente o estado da arte, incluindo soluções do domínio público e proprietárias, destacando os pontos fortes e fraquezas de cada uma. Para além da análise das tecnologias utilizadas no desenvolvimento de cada solução, este estudo teve contributos determinantes na análise de requisitos efetuada. Nomeadamente, permitiu-nos identificar funcionalidades inovadoras e com elevado valor para os utilizadores. O resultado é um sistema de informação capaz de gerir todas as operações de um departamento de radiologia, incluindo gestão administrativa de utentes, agendamento de exames, realização de relatórios clínicos, entre outras. Em termos de características inovadoras destaca-se o módulo de relatório que permite carregar novos modelos de relatórios com o sistema em produção e a sua exportação para o formato standard DICOM-SR, permitindo desta forma a sua integração com as imagens no repositório PACS. Em termos tecnológicos, desenvolveu-se uma aplicação web multiplataforma que segue uma arquitetura modular orientada a serviços e que oferece uma abstração relativamente à camada de persistência de dados.The widespread adoption of digital medical images in various types of health institutions, has raised new problems regarding data and processes management. The standardisation of these scenarios has been subject of attention in the last decades, resulting in the development and promotion of standards such as DICOM and HL7. Currently, there are two kinds of information systems in medical imaging laboratories, that must operate in a collaborative manner, RIS which is responsible for managing the administrative tasks and PACS that manage images and associated information. This dissertation aimed to design and implement an RIS solution based on tools with no use restriction or open source. We begin by studying in detail the state of the art, including the open source and proprietary solutions, highlighting the strengths and weaknesses of each one. In addition to analysing the technologies used in the development of each solution, this study provided decisive contributions, regarding the project requirements. In particular, it allowed us to identify innovative features with high value to users. The achieved solution is an information system capable of managing all operations in a radiology department, including administrative management of patients, exam scheduling, conducting clinical reports, among others. Regarding innovative features, the reporting module stands out, since it allows to upload new report templates into the system and export these clinical reports in the DICOM-SR standard, thus allowing their integration with the images in a PACS repository. Regarding the technologies aspect, it was developed a multi-platform web application that follows a modular service-oriented architecture and also provides an abstraction in regard to the data persistence layer

    Analysis of New Concepts and Definitions in DICOM Second Generation Radiotherapy Objects Based on an Experimental Implementation

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    This thesis examines the new major concepts for communicating radiotherapy-related data with DICOM, introduced in Supplement 147. As the existing DICOM information objects, used to transfer radiotherapy-related information, are mostly overloaded and static, new concepts to describe this data are developed at the moment in Supplement 147. These concepts facilitate a more convenient representation of new treatment devices and treatment techniques in DICOM and solve other issues with first-generation DICOM RT objects. Hence Supplement 147 is replacing the entire working concept strategy for a complete domain, and the supplement itself is extensive in comparison to other supplements, an overview whether all these concepts work together just by examining them on a drawing board is hardly possible. Therefore, this thesis investigates the information separation into different Information Object Definitions (IODs), the new radiation prescription object and the new concept to enable abstract access to volumetric objects, which are considered to be the major conceptual changes

    Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

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    This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually. This is an open access book

    A situated method for modelling and analysing the efficiency of cognitive activity during the radiology reporting workflow using eye-tracking

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    The success of modern medical imaging systems has created a data overload problem, where an ever-increasing number of examinations, generate more images per study, which all need to be evaluated by radiologists or other reporting practitioners. This operational bottleneck hasthe potentialto create fatigue and burnout due to the high mental workload that is required to keep up with the demand. The focus of this problem centres around the cognitive complexity of the radiology reporting workflow, and the associated workstation interactions involved in diagnostic report generation. There has been a significant body of work evaluating the behaviour of radiologists using controlled laboratory-based techniques, but these non-naturalistic studies fail to address the highly context dependant nature of the radiology reporting workflow. For example, the early eye-tracking work of Charmody et al; the psychometric studies by Krupinksi et al; and also the workstation interaction evaluations of Moise et al; whilst highly principled, can be all be questioned on the grounds of ecological validity and authenticity. This thesis asserts that the only way to truly understand and resolve the radiology data overload problem, is by developing a situated method for observing the reporting workflow that can evaluate the behaviours of the reporting clinicians in relation to their authentic reporting context. To this end, this study has set out to develop a new approach for observing and analysing the cognitive activities of the reporters relative to the demands of their genuine working environment, and supported through the application of a Critical Realist’s perspective to naturalistic workplace observations. This goal was achieved through the development of four key project deliverables: • An in-depth exploratory study of the radiology overload problem based on an extensive literature review and situated observations of authentic reporting workflows. • A descriptive hierarchical activity modelof the reporting workflow that can be understood by both clinicians, application designers and researchers. • A generalised methodology and research protocolfor conducting situated observations of the radiology reporting workflow, using an analysis based on the process tracing of sequencesof Object Related Actions, captured with eye-tracking and multimodal recordings. • A set of case studies demonstrating the applicability of the research protocol involving 5 Radiology Consultants, 2 Radiology Registrars and one Reporting Radiographer at a single NHS Hospital within the UK. The final workflow evaluation of the case studies demonstrated that activities such as error correction, and the collection of supporting radiological information from previous studies is complex, time consuming and cognitively demanding. These types of activities are characterised by long, low utility actions that correspond to what Kahneman refers to as “Thinking Slow”. Also, the participants appeared to be self-optimising their workflow via a sparse use of complex functionality and system tools. From these observations, the author recommends that any intervention that can reduce the number and the duration of the object related actions used to produce radiology reports, will reduce cognitive load, increase overall efficiency, and go some way to alleviate the data overload problem. 4 This study establishes a new set of situated techniques that are able to capture and quantify the complex dynamicactivities that make up the radiology reporting workflow. Itis hoped that the ability to distil usefuland impactful insightsfrom the user’s workstation behaviours can be used as the basis for further development in the area of workflow analysis and redesign, which will ultimately improve the working lives of Radiologists and other Reporting Clinicians. Lastly, the generic nature of these techniques make them amenable for use within any type of complex sociotechnical human factors study related to the cognitive efficiency of the user
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