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Background: Digital transformation, particularly the integration of medical imaging with clinical data, is vital in personalized medicine. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardizes health data. However, integrating medical imaging remains a challenge., Objective: This study proposes a method for combining medical imaging data with the OMOP CDM to improve multimodal research., Methods: Our approach included the analysis and selection of digital imaging and communications in medicine header tags, validation of data formats, and alignment according to the OMOP CDM framework. The Fast Healthcare Interoperability Resources ImagingStudy profile guided our consistency in column naming and definitions. Imaging Common Data Model (I-CDM), constructed using the entity-attribute-value model, facilitates scalable and efficient medical imaging data management. For patients with lung cancer diagnosed between 2010 and 2017, we introduced 4 new tables-IMAGING_STUDY, IMAGING_SERIES, IMAGING_ANNOTATION, and FILEPATH-to standardize various imaging-related data and link to clinical data., Results: This framework underscores the effectiveness of I-CDM in enhancing our understanding of lung cancer diagnostics and treatment strategies. The implementation of the I-CDM tables enabled the structured organization of a comprehensive data set, including 282,098 IMAGING_STUDY, 5,674,425 IMAGING_SERIES, and 48,536 IMAGING_ANNOTATION records, illustrating the extensive scope and depth of the approach. A scenario-based analysis using actual data from patients with lung cancer underscored the feasibility of our approach. A data quality check applying 44 specific rules confirmed the high integrity of the constructed data set, with all checks successfully passed, underscoring the reliability of our findings., Conclusions: These findings indicate that I-CDM can improve the integration and analysis of medical imaging and clinical data. By addressing the challenges in data standardization and management, our approach contributes toward enhancing diagnostics and treatment strategies. Future research should expand the application of I-CDM to diverse disease populations and explore its wide-ranging utility for medical conditions., (©Hyerim Ji, Seok Kim, Leonard Sunwoo, Sowon Jang, Ho-Young Lee, Sooyoung Yoo. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 12.07.2024.)

Importance: Medical imaging increases the workload involved in writing reports. Given the lack of a standardized format for reports, reports are not easily used as communication tools. Objective: During medical team-patient communication, the descriptions in reports also need to be understood. Automatically generated imaging reports with rich and understandable information can improve medical quality. Design, setting, and participants: The image analysis theory of Panofsky and Shatford from the perspective of image metadata was used in this study to establish a medical image interpretation template (MIIT) for automated image report generation. Main outcomes and measures: The image information included digital imaging and communications in medicine (DICOM), reporting and data systems (RADSs), and image features used in computer-aided diagnosis (CAD). The utility of the images was evaluated by a questionnaire survey to determine whether the image content could be better understood. Results: In 100 responses, exploratory factor analysis revealed that the factor loadings of the facets were greater than 0.5, indicating construct validity, and the overall Cronbach's alpha was 0.916, indicating reliability. No significant differences were noted according to sex, age or education. Conclusions and relevance: Overall, the results show that MIIT is helpful for understanding the content of medical images., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

A clinically meaningful use of structured reporting, which in the opinion of numerous scientific societies and experts is a very important prerequisite for the further development of radiological findings, especially under quality aspects, requires corresponding standards for implementation in IT systems. In addition to DICOM ("digital imaging and communication in medicine"), these are other standards for coding, for example RadLex ("radiological lexicon") or the specification of so-called interoperability profiles, as they are being developed by IHE ("integrating the healthcare enterprise"). The management of radiology report templates (MRRT) profiles is the central building block for this. The building blocks for efficient IT implementation, which also allow harmonization, for example at a national level, are currently available. Users in radiology should familiarize themselves with them and demand appropriate solutions from manufacturers., (© 2021. Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Background: Adoption of the Digital Imaging and Communications in Medicine (DICOM) standard for whole slide images (WSIs) has been slow, despite significant time and effort by standards curators. One reason for the lack of adoption is that there are few tools which exist that can meet the requirements of WSIs, given an evolving ecosystem of best practices for implementation. Eventually, vendors will conform to the specification to ensure enterprise interoperability, but what about archived slides? Millions of slides have been scanned in various proprietary formats, many with examples of rare histologies. Our hypothesis is that if users and developers had access to easy to use tools for migrating proprietary formats to the open DICOM standard, then more tools would be developed as DICOM first implementations., Methods: The technology we present here is dicom_wsi, a Python based toolkit for converting any slide capable of being read by the OpenSlide library into DICOM conformant and validated implementations. Moreover, additional postprocessing such as background removal, digital transformations (e.g., ink removal), and annotation storage are also described. dicom_wsi is a free and open source implementation that anyone can use or modify to meet their specific purposes., Results: We compare the output of dicom_wsi to two other existing implementations of WSI to DICOM converters and also validate the images using DICOM capable image viewers., Conclusion: dicom_wsi represents the first step in a long process of DICOM adoption for WSI. It is the first open source implementation released in the developer friendly Python programming language and can be freely downloaded at ., Competing Interests: There are no conflicts of interest., (Copyright: © 2021 Journal of Pathology Informatics.)

We present a roadmap for integrating artificial intelligence (AI)-based image analysis algorithms into existing radiology workflows such that (1) radiologists can significantly benefit from enhanced automation in various imaging tasks due to AI, and (2) radiologists' feedback is utilized to further improve the AI application. This is achieved by establishing three maturity levels where (1) research enables the visualization of AI-based results/annotations by radiologists without generating new patient records; (2) production allows the AI-based system to generate results stored in an institution's picture-archiving and communication system; and (3) feedback equips radiologists with tools for editing the AI inference results for periodic retraining of the deployed AI systems, thereby allowing continuous organic improvement of AI-based radiology-workflow solutions. A case study (i.e., detection of brain metastases with T1-weighted contrast-enhanced three-dimensional MRI) illustrates the deployment details of a particular AI-based application according to the aforementioned maturity levels. It is shown that the given AI application significantly improves with feedback coming from radiologists; the number of incorrectly detected brain metastases (false positives) decreases from 14.2 to 9.12 per patient with the number of subsequently annotated datasets increasing from 93 to 217 as a result of radiologist adjudication., (© The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.)

Despite recently organized Digital Imaging and Communications in Medicine (DICOM) testing and demonstration events involving numerous participating vendors, it is still the case that scanner manufacturers, software developers, and users continue to depend on proprietary file formats rather than adopting the standard DICOM whole slide microscopic image object. Many proprietary formats are Tagged Image File Format (TIFF) based, and existing applications and libraries can read tiled TIFF files. The sluggish adoption of DICOM for whole slide image encoding can be temporarily mitigated by the use of dual-personality DICOM-TIFF files. These are compatible with the installed base of TIFF-based software, as well as newer DICOM-based software. The DICOM file format was deliberately designed to support this dual-personality capability for such transitional situations, although it is rarely used. Furthermore, existing TIFF files can be converted into dual-personality DICOM-TIFF without changing the pixel data. This paper demonstrates the feasibility of extending the dual-personality concept to multiframe-tiled pyramidal whole slide images and explores the issues encountered. Open source code and sample converted images are provided for testing., Competing Interests: There are no conflicts of interest.

Objectives: The aim of this study was to develop and verify a personal computer-based software tool for calculating uniformity indices of gamma camera., Materials and Methods: The program was developed in MATLAB R2013b under Microsoft Windows operating system. Noise-less digital phantoms with known uniformity parameters were used to verify the accuracy of the program. Two hundred and forty-four Co-57 flood source images were acquired on Symbia T6 and Discovery nuclear medicine/computed tomography 670. The uniformity indices of these images were determined with their respective vendor's software and also by the tool developed. Bland-Altman plots were used for measuring the agreements between the developed program and the vendor's program for the calculation of uniformity indices., Results: The tool for calculating uniformity indices was found to be accurate. Uniformity indices measured with the tool revealed a very good correlation with vendor's software based on Bland-Altman analysis, as almost all measurements were within the ±2 standard deviation range., Conclusion: The software tool for calculation of uniformity indices is accurate, and the uniformity indices calculated by it are in agreement with uniformity indices calculated by the vendor's software., Competing Interests: There are no conflicts of interest.

Introduction: Two-dimensional strain analysis is a powerful analysis modality, however, clinical utilization has been limited by variability between different analysis systems and operators. We compared strain in adults and children using vendor-specific and vendor-independent software to evaluate variability., Methods: One hundred and ten subjects (50/110 pediatric, 80/110 normal left ventricular function) had echocardiograms with a General Electric ultrasound scanner between September 2010 and January 2012. Left ventricular longitudinal strain was derived with EchoPAC (General Electric, v10.8.1), a vendor-specific software, and Velocity Vector Imaging (Siemens, v3.5), which is vendor-independent. Three independent readers analyzed all the echocardiograms yielding 330 datasets., Results: Mean left ventricular global longitudinal Lagrangian strain was -18.1 ± SD 4.4% for EchoPAC and -15.3 ± SD 4.1% for Velocity Vector Imaging. Velocity Vector Imaging yielded lower absolute global longitudinal Lagrangian strain by mean 2.9 (±SD 2.7, p < 0.0001), and lower regional longitudinal strain. These differences persisted in normal subjects versus those with cardiomyopathy. Longitudinal strain differences were slightly higher in the pediatric cohort. There was no significant difference in inter-observer longitudinal strain and a small difference in intra-observer strain between analysis systems. On repeat measurements, a significant change in global longitudinal Lagrangian strain occurred after the difference exceeded 3-5 strain points for EchoPAC and Velocity Vector Imaging, respectively., Conclusion: Velocity Vector Imaging produces lower left ventricular longitudinal strain values versus EchoPAC for the same echo images. Both systems have similar inter-observer variability, Velocity Vector Imaging slightly higher intra-observer variability. A statistically significant change in global longitudinal Lagrangian strain occurs with changes >3-5 strain points on repeat measurements. Strain values between the systems are not interchangeable.

The digital imaging and communications in medicine (DICOM) information model combines pixel data and its metadata in a single object. There are user scenarios that only need metadata manipulation, such as deidentification and study migration. Most picture archiving and communication system use a database to store and update the metadata rather than updating the raw DICOM files themselves. The multiseries DICOM (MSD) format separates metadata from pixel data and eliminates duplicate attributes. This work promotes storing DICOM studies in MSD format to reduce the metadata processing time. A set of experiments are performed that update the metadata of a set of DICOM studies for deidentification and migration. The studies are stored in both the traditional single frame DICOM (SFD) format and the MSD format. The results show that it is faster to update studies' metadata in MSD format than in SFD format because the bulk data is separated in MSD and is not retrieved from the storage system. In addition, it is space efficient to store the deidentified studies in MSD format as it shares the same bulk data object with the original study. In summary, separation of metadata from pixel data using the MSD format provides fast metadata access and speeds up applications that process only the metadata.

Objective: To design, build, and evaluate a storage model able to manage heterogeneous digital imaging and communications in medicine (DICOM) images. The model must be simple, but flexible enough to accommodate variable content without structural modifications; must be effective on answering query/retrieval operations according to the DICOM standard; and must provide performance gains on querying/retrieving content to justify its adoption by image-related projects., Methods: The proposal adapts the original decomposed storage model, incorporating structural and organizational characteristics present in DICOM image files. Tag values are stored according to their data types/domains, in a schema built on top of a standard relational database management system (RDBMS). Evaluation includes storing heterogeneous DICOM images, querying metadata using a variable number of predicates, and retrieving full-content images for different hierarchical levels., Results and Discussion: When compared to a well established DICOM image archive, the proposal is 0.6-7.2 times slower in storing content; however, in querying individual tags, it is about 48.0% faster. In querying groups of tags, DICOM decomposed storage model (DCMDSM) is outperformed in scenarios with a large number of tags and low selectivity (being 66.5% slower); however, when the number of tags is balanced with better selectivity predicates, the performance gains are up to 79.1%. In executing full-content retrieval, in turn, the proposal is about 48.3% faster., Conclusions: DCMDSM is a model built for the storage of heterogeneous DICOM content, based on a straightforward database design. The results obtained through its evaluation attest its suitability as a storage layer for projects where DICOM images are stored once, and queried/retrieved whenever necessary., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Introduction: The use of open source technologies to create collaboration platforms can produce huge advantages with small investment., Materials and Methods: We set up a telemedicine network for a healthcare district with typical centralization issues of developing countries. Our network was built using broadband Internet connection, and the digital divide in rural areas was reduced by means of wireless Internet connection. A software infrastructure was deployed on the network to implement the collaboration platform among different healthcare facilities., Results: We obtained an integrated platform with modest investment in hardware and operating systems and no costs for application software. Messaging, content management, information sharing, and videoconferencing are among the available services of the infrastructure. Furthermore, open source software is managed and continuously updated by active communities, making it possible to obtain systems similar to commercial ones in terms of quality and reliability., Conclusions: As the use of free software in public administration is being widely promoted across the European Union, our experience may provide an example to implement similar infrastructures in the field of healthcare and welfare.

Premenstrual disorder (PMDD) affects around 5% of women in childbearing ages. An increased sensitivity in emotion processing areas of the brain to variations in ovarian steroid levels has been suggested as part of the pathophysiology in PMDD, but prior neuroimaging studies of emotion processing are yet inconclusive. Previous behavioral studies of women with PMDD have, however, reported enhanced luteal phase startle responsivity during emotional anticipation. Here we used functional magnetic resonance imaging (fMRI) to investigate central neural circuitry activity during anticipation of, and exposure to, emotional stimuli across the menstrual cycle in women with and without PMDD. As compared to healthy controls, women with PMDD displayed significantly enhanced reactivity in the prefrontal cortex during anticipation of, but not exposure to, negative emotional stimuli during the luteal phase. In PMDD patients, BOLD reactivity during anticipation or viewing of negative emotional stimuli was not dependent on absolute levels of estradiol or progesterone. However, progesterone levels were positively correlated with emotion-induced reactivity in the dorsolateral prefrontal cortex to positive emotional stimuli. These findings suggest that cortical emotional circuitry reactivity during anticipation is altered in PMDD during the luteal phase, which might be part of the pathophysiology behind the emotional symptoms or lack of emotional control reported by women with PMDD., (© 2013 Elsevier B.V. and ECNP. All rights reserved.)

Although widely touted as a replacement for glass slides and microscopes in pathology, digital slides present major challenges in data storage, transmission, processing and interoperability. Since no universal data format is in widespread use for these images today, each vendor defines its own proprietary data formats, analysis tools, viewers and software libraries. This creates issues not only for pathologists, but also for interoperability. In this paper, we present the design and implementation of OpenSlide, a vendor-neutral C library for reading and manipulating digital slides of diverse vendor formats. The library is extensible and easily interfaced to various programming languages. An application written to the OpenSlide interface can transparently handle multiple vendor formats. OpenSlide is in use today by many academic and industrial organizations world-wide, including many research sites in the United States that are funded by the National Institutes of Health.