Creation and Curation of the Society of Imaging Informatics in Medicine Hackathon Dataset.

In order to support innovation, the Society of Imaging Informatics in Medicine (SIIM) elected to create a collaborative computing experience called a "hackathon." The SIIM Hackathon has always consisted of two components, the event itself and the infrastructure and resources provided to the participants. In 2014, SIIM provided a collection of servers to participants during the annual meeting. After initial server setup, it was clear that clinical and imaging "test" data were also needed in order to create useful applications. We outline the goals, thought process, and execution behind the creation and maintenance of the clinical and imaging data used to create DICOM and FHIR Hackathon resources.

DICOMweb™: Background and Application of the Web Standard for Medical Imaging.

This paper describes why and how DICOM, the standard that has been the basis for medical imaging interoperability around the world for several decades, has been extended into a full web technology-based standard, DICOMweb. At the turn of the century, healthcare embraced information technology, which created new problems and new opportunities for the medical imaging industry; at the same time, web technologies matured and began serving other domains well. This paper describes DICOMweb, how it extended the DICOM standard, and how DICOMweb can be applied to problems facing healthcare applications to address workflow and the changing healthcare climate.

Conversion of Radiology Reporting Templates to the MRRT Standard.

In 2013, the Integrating the Healthcare Enterprise (IHE) Radiology workgroup developed the Management of Radiology Report Templates (MRRT) profile, which defines both the format of radiology reporting templates using an extension of Hypertext Markup Language version 5 (HTML5), and the transportation mechanism to query, retrieve, and store these templates. Of 200 English-language report templates published by the Radiological Society of North America (RSNA), initially encoded as text and in an XML schema language, 168 have been converted successfully into MRRT using a combination of automated processes and manual editing; conversion of the remaining 32 templates is in progress. The automated conversion process applied Extensible Stylesheet Language Transformation (XSLT) scripts, an XML parsing engine, and a Java servlet. The templates were validated for proper HTML5 and MRRT syntax using web-based services. The MRRT templates allow radiologists to share best-practice templates across organizations and have been uploaded to the template library to supersede the prior XML-format templates. By using MRRT transactions and MRRT-format templates, radiologists will be able to directly import and apply templates from the RSNA Report Template Library in their own MRRT-compatible vendor systems. The availability of MRRT-format reporting templates will stimulate adoption of the MRRT standard and is expected to advance the sharing and use of templates to improve the quality of radiology reports.

REST enabling the report template library.

Structured reporting, created when a standardized template with organized subheadings is combined with relevant observations of a diagnostic study into a meaningful result, has the potential to raise both the quality and the predictability of the radiologist report, revolutionizing the workflow and its outcomes. These templates contain great value, as they carve a path based on best practice for the radiologist to follow, and thus should be shared, reviewed, and improved. Unfortunately, these templates are often not shareable today due to a lack of standards for describing and transporting templates. This paper outlines and discusses an appropriate and effective electronic method for transporting radiology report templates using of the style of representational state transfer (REST). Enabling a structured radiology report template library with REST enables just-in-time accessibility of templates, achieving efficiencies and effectiveness.

Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies.

Purpose: XNAT is an informatics software platform to support imaging research, particularly in the context of large, multicentre studies of the type that are essential to validate quantitative imaging biomarkers. XNAT provides import, archiving, processing and secure distribution facilities for image and related study data. Until recently, however, modern data visualisation and annotation tools were lacking on the XNAT platform. We describe the background to, and implementation of, an integration of the Open Health Imaging Foundation (OHIF) Viewer into the XNAT environment. We explain the challenges overcome and discuss future prospects for quantitative imaging studies. Materials and methods: The OHIF Viewer adopts an approach based on the DICOM web protocol. To allow operation in an XNAT environment, a data-routing methodology was developed to overcome the mismatch between the DICOM and XNAT information models and a custom viewer panel created to allow navigation within the viewer between different XNAT projects, subjects and imaging sessions. Modifications to the development environment were made to allow developers to test new code more easily against a live XNAT instance. Major new developments focused on the creation and storage of regions-of-interest (ROIs) and included: ROI creation and editing tools for both contour- and mask-based regions; a "smart CT" paintbrush tool; the integration of NVIDIA's Artificial Intelligence Assisted Annotation (AIAA); the ability to view surface meshes, fractional segmentation maps and image overlays; and a rapid image reader tool aimed at radiologists. We have incorporated the OHIF microscopy extension and, in parallel, introduced support for microscopy session types within XNAT for the first time. Results: Integration of the OHIF Viewer within XNAT has been highly successful and numerous additional and enhanced tools have been created in a programme started in 2017 that is still ongoing. The software has been downloaded more than 3700 times during the course of the development work reported here, demonstrating the impact of the work. Conclusions: The OHIF open-source, zero-footprint web viewer has been incorporated into the XNAT platform and is now used at many institutions worldwide. Further innovations are envisaged in the near future.

Integrating Al Algorithms into the Clinical Workflow.

Integration of artificial intelligence (AI) applications within clinical workflows is an important step for leveraging developed AI algorithms. In this report, generalizable components for deploying AI systems into clinical practice are described that were implemented in a clinical pilot study using lymphoscintigraphy examinations as a prospective use case (July 1, 2019-October 31, 2020). Deployment of the AI algorithm consisted of seven software components, as follows: (a) image delivery, (b) quality control, (c) a results database, (d) results processing, (e) results presentation and delivery, (f) error correction, and (g) a dashboard for performance monitoring. A total of 14 users used the system (faculty radiologists and trainees) to assess the degree of satisfaction with the components and overall workflow. Analyses included the assessment of the number of examinations processed, error rates, and corrections. The AI system processed 1748 lymphoscintigraphy examinations. The system enabled radiologists to correct 146 AI results, generating real-time corrections to the radiology report. All AI results and corrections were successfully stored in a database for downstream use by the various integration components. A dashboard allowed monitoring of the AI system performance in real time. All 14 survey respondents "somewhat agreed" or "strongly agreed" that the AI system was well integrated into the clinical workflow. In all, a framework of processes and components for integrating AI algorithms into clinical workflows was developed. The implementation described could be helpful for assessing and monitoring AI performance in clinical practice. Keywords: PACS, Computer Applications-General (Informatics), Diagnosis © RSNA, 2021.