ArticleJournal of digital imaging2021
The Importance of Body Part Labeling to Enable Enterprise Imaging: A HIMSS-SIIM Enterprise Imaging Community Collaborative White Paper.
Article in Journal of digital imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 21 citations in OpenAlex.
- In search of truth: evaluating concordance of AI-based anatomy segmentation models.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Deep learning models for radiography body-part classification and chest radiograph projection/orientation classification: a multi-institutional study.European radiology · 2026Article
- A simple and effective approach for body part recognition on CT scans based on projection estimation.Scientific reports · 2025Article
- Implementing a Photodocumentation Program.Journal of imaging informatics in medicine · 2025Article
- Report of the HIMSS-SIIM Enterprise Imaging Community Data Standards Evaluation Workgroup: Anatomic Ontology Assessment.Journal of imaging informatics in medicine · 2024Article
- Celebrating 10 Years of the HIMSS-SIIM Enterprise Imaging Community and Enterprise Imaging Informatics.Journal of imaging informatics in medicine · 2024Article
- Large-Scale Integration of DICOM Metadata into HL7-FHIR for Medical Research.Methods of information in medicine · 2024Article
- Special Challenges of Sensitive Images: A HIMSS-SIIM Enterprise Imaging Community Whitepaper.Journal of imaging informatics in medicine · 2024Article
- HIMSS-SIIM Enterprise Imaging Community White Papers: Reflections and Future Directions.Journal of imaging informatics in medicine · 2024Article
- A practical guide to clinical photography prior to skin biopsy: key tips and proposed workflow.Archives of dermatological research · 2023Article
- Lessons Learned in Change Management in Deploying Novel Informatics Solutions: Experience Implementing a Point-of-Care Patient Photography System with Radiography.Journal of digital imaging · 2023Article
- Deep Learning Body Region Classification of MRI and CT Examinations.Journal of digital imaging · 2023Article
- How Image Exchange Breaks Down: the Image Library Perspective.Journal of digital imaging · 2022Article
- Visible Light Imaging: Clinical Aspects with an Emphasis on Medical Photography-a HIMSS-SIIM Enterprise Imaging Community Whitepaper.Journal of digital imaging · 2022Article
- Federated Deep Learning to More Reliably Detect Body Part for Hanging Protocols, Relevant Priors, and Workflow Optimization.Journal of digital imaging · 2022Article
- Biomedical Ontologies to Guide AI Development in Radiology.Journal of digital imaging · 2021Review
- Multispecialty Enterprise Imaging Workgroup Consensus on Interactive Multimedia Reporting Current State and Road to the Future: HIMSS-SIIM Collaborative White Paper.Journal of digital imaging · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order for enterprise imaging to be successful across a multitude of specialties, systems, and sites, standards are essential to categorize and classify imaging data. The HIMSS-SIIM Enterprise Imaging Community believes that the Digital Imaging Communications in Medicine (DICOM) Anatomic Region Sequence, or its equivalent in other data standards, is a vital data element for this role, when populated with standard coded values. We believe that labeling images with standard Anatomic Region Sequence codes will enhance the user's ability to consume data, facilitate interoperability, and allow greater control of privacy. Image consumption-when a user views a patient's images, he or she often wants to see relevant comparison images of the same lesion or anatomic region for the same patient automatically presented. Relevant comparison images may have been acquired from a variety of modalities and specialties. The Anatomic Region Sequence data element provides a basis to allow for efficient comparison in both instances. Interoperability-as patients move between health care systems, it is important to minimize friction for data transfer. Health care providers and facilities need to be able to consume and review the increasingly large and complex volume of data efficiently. The use of Anatomic Region Sequence, or its equivalent, populated with standard values enables seamless interoperability of imaging data regardless of whether images are used within a site or across different sites and systems. Privacy-as more visible light photographs are integrated into electronic systems, it becomes apparent that some images may need to be sequestered. Although additional work is needed to protect sensitive images, standard coded values in Anatomic Region Sequence support the identification of potentially sensitive images, enable facilities to create access control policies, and can be used as an interim surrogate for more sophisticated rule-based or attribute-based access control mechanisms. To satisfy such use cases, the HIMSS-SIIM Enterprise Imaging Community encourages the use of a pre-existing body part ontology. Through this white paper, we will identify potential challenges in employing this standard and provide potential solutions for these challenges.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.