On the Use of XML in Medical Imaging Web-Based Applications

The rapid growth of digital technology in medical fields over recent years has increased the need for applications able to manage patient medical records, imaging data, and chart information. Web-based applications are implemented with the purpose to link digital databases, storage and transmission protocols, management of large volumes of data and security concepts, allowing the possibility to read, analyze, and even diagnose remotely from the medical center where the information was acquired. The objective of this paper is to analyze the use of the Extensible Markup Language (XML) language in web-based applications that aid in diagnosis or treatment of patients, considering how this protocol allows indexing and exchanging the huge amount of information associated with each medical case. The purpose of this paper is to point out the main advantages and drawbacks of the XML technology in order to provide key ideas for future web-based applicationsPeer ReviewedPostprint (author's final draft

Medical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset Session.

At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities

Towards structured sharing of raw and derived neuroimaging data across existing resources

Data sharing efforts increasingly contribute to the acceleration of scientific discovery. Neuroimaging data is accumulating in distributed domain-specific databases and there is currently no integrated access mechanism nor an accepted format for the critically important meta-data that is necessary for making use of the combined, available neuroimaging data. In this manuscript, we present work from the Derived Data Working Group, an open-access group sponsored by the Biomedical Informatics Research Network (BIRN) and the International Neuroimaging Coordinating Facility (INCF) focused on practical tools for distributed access to neuroimaging data. The working group develops models and tools facilitating the structured interchange of neuroimaging meta-data and is making progress towards a unified set of tools for such data and meta-data exchange. We report on the key components required for integrated access to raw and derived neuroimaging data as well as associated meta-data and provenance across neuroimaging resources. The components include (1) a structured terminology that provides semantic context to data, (2) a formal data model for neuroimaging with robust tracking of data provenance, (3) a web service-based application programming interface (API) that provides a consistent mechanism to access and query the data model, and (4) a provenance library that can be used for the extraction of provenance data by image analysts and imaging software developers. We believe that the framework and set of tools outlined in this manuscript have great potential for solving many of the issues the neuroimaging community faces when sharing raw and derived neuroimaging data across the various existing database systems for the purpose of accelerating scientific discovery

A study to understand the acceptance of DICOM Structured Reports on Breast Imaging

Purpose: To create a Digital Imaging and Communication in Medicine Structured Reports (DICOM-SR) Repository and compare the acceptance of Free Text (FT) versus Structured Reports (SR) in communication of Breast Imaging findings. Materials and Methods: It was conceptualized the MamoCatalogue to the structuring of the Reports and the SR were converted into DICOM-SR and integrated with Dicoogle. After that, seven representative Breast Imaging Reports were selected and evaluated by a group of 25 Physicians. Each Physician evaluated the seven Reports, in FT and SR with a 3 months timelag, about their, Structure, Clarity and assertiveness, Diagnostic/Recommendations, Easiness of reading, Full reading, Partially reading with Breast Imaging Reporting and Data System (BI-RADS) focus and Ambiguity. Results: A DICOM-SR Repository was created and the assessment of the acceptance of the FT vs. SR revealed that there is a global trend favoring FT. Nevertheless, a group wise analysis revealed that for Gynaecologists and General Practitioners (GP) the differences between FT and SR weren't significant, unlike what happens with Radiologists. Conclusion: The DICOM-SR Repository allows the query/retrieve data for Reports and the communication with Gynaecologists and GP by SR was satisfactory. Although, Radiologists acceptance must be reinforced upon global communication and management strategy

Description and Experience of the Clinical Testbeds

This deliverable describes the up-to-date technical environment at three clinical testbed demonstrator sites of the 6WINIT Project, including the adapted clinical applications, project components and network transition technologies in use at these sites after 18 months of the Project. It also provides an interim description of early experiences with deployment and usage of these applications, components and technologies, and their clinical service impact

Knowledge discovery methodology for medical reports

Medical reports contain valuable information, not only for the patient that waits for the results but also the latent knowledge that is possible to extract from them. The recent introduction of standard structured formats like the Digital Imaging and Communications in Medicine Structured Report and the Clinical Document Architecture Health Level Seven provide an efficient generation, distribution, and management mechanism. Also, they provide an intuitive and effective manner of information representation, unlike the traditional plain text format. In this paper we present a knowledge discovery methodology for structured report interchange based on plain text medical reports using YALE, a leading open-source data mining tool and Open-ESB platform that provides conversion, parsing, different protocols and message formats interchange capabilities.Centro de Imagiologia da Trindade (CIT

Increasing the Efficiency on Producing Radiology Reports for Breast Cancer Diagnosis by Means of Structured Reports

Background: Radiology reports are commonly written on free-text using voice recognition devices. Structured reports (SR) have a high potential but they are usually considered more difficult to fill-in so their adoption in clinical practice leads to a lower efficiency. However, some studies have demonstrated that in some cases, producing SRs may require shorter time than plain-text ones. This work focuses on the definition and demonstration of a methodology to evaluate the productivity of software tools for producing radiology reports. A set of SRs for breast cancer diagnosis based on BI-RADS have been developed using this method. An analysis of their efficiency with respect to free-text reports has been performed. Material and Methods: The methodology proposed compares the Elapsed Time (ET) on a set of radiological reports. Free-text reports are produced with the speech recognition devices used in the clinical practice. Structured reports are generated using a web application generated with TRENCADIS framework. A team of six radiologists with three different levels of experience in the breast cancer diagnosis was recruited. These radiologists performed the evaluation, each one introducing 50 reports for mammography, 50 for ultrasound scan and 50 for MRI using both approaches. Also, the Relative Efficiency (REF) was computed for each report, dividing the ET of both methods. We applied the T-Student (T-S) test to compare the ETs and the ANOVA test to compare the REFs. Both tests were computed using the SPSS software. Results: The study produced three DICOM-SR templates for Breast Cancer Diagnosis on mammography, ultrasound and MRI, using RADLEX terms based on BIRADs 5th edition. The T-S test on radiologists with high or intermediate profile, showed that the difference between the ET was only statistically significant for mammography and ultrasound. The ANOVA test performed grouping the REF by modalities, indicated that there were no significant differences between mammograms and ultrasound scans, but both have significant statistical differences with MRI. The ANOVA test of the REF for each modality, indicated that there were only significant differences in Mammography (ANOVA p = 0.024) and Ultrasound (ANOVA p = 0.008). The ANOVA test for each radiologist profile, indicated that there were significant differences on the high profile (ANOVA p = 0.028) and medium (ANOVA p = 0.045). Conclusions: In this work, we have defined and demonstrated a methodology to evaluate the productivity of software tools for producing radiology reports in Breast Cancer. We have evaluated that adopting Structured Reporting in mammography and ultrasound studies in breast cancer diagnosis improves the performance in producing reports.INDIGO - DataCloud receives funding from the European Union's Horizon 2020 research and innovation programme under grant agreement RIA 653549.Segrelles Quilis, JD.; Medina, R.; Blanquer Espert, I.; Marti Bonmati, L. (2017). Increasing the Efficiency on Producing Radiology Reports for Breast Cancer Diagnosis by Means of Structured Reports. Methods of Information in Medicine. 56:1-13. https://doi.org/10.3414/ME16-01-0091S1135