Literature DB >> 33481143

The Importance of Body Part Labeling to Enable Enterprise Imaging: A HIMSS-SIIM Enterprise Imaging Community Collaborative White Paper.

Alexander J Towbin1, Christopher J Roth2, Cheryl A Petersilge3, Kimberley Garriott4, Kenneth A Buckwalter5, David A Clunie6.   

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.

Entities:  

Keywords:  DICOM; Enterprise Imaging; Medical Photography; Ontology; Photo documentation ; Standards

Year:  2021        PMID: 33481143     DOI: 10.1007/s10278-020-00415-0

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  1 in total

1.  Determining scanned body part from DICOM study description for relevant prior study matching.

Authors:  Thusitha Mabotuwana; Yuechen Qian
Journal:  Stud Health Technol Inform       Date:  2013
  1 in total
  4 in total

1.  Federated Deep Learning to More Reliably Detect Body Part for Hanging Protocols, Relevant Priors, and Workflow Optimization.

Authors:  Ross W Filice; Anouk Stein; Ian Pan; George Shih
Journal:  J Digit Imaging       Date:  2022-01-11       Impact factor: 4.056

2.  How Image Exchange Breaks Down: the Image Library Perspective.

Authors:  Christopher J Roth; Hope H Harten; Matt Dewey; Don K Dennison
Journal:  J Digit Imaging       Date:  2022-08-01       Impact factor: 4.903

Review 3.  Multispecialty Enterprise Imaging Workgroup Consensus on Interactive Multimedia Reporting Current State and Road to the Future: HIMSS-SIIM Collaborative White Paper.

Authors:  Christopher J Roth; David A Clunie; David J Vining; Seth J Berkowitz; Alejandro Berlin; Jean-Pierre Bissonnette; Shawn D Clark; Toby C Cornish; Monief Eid; Cree M Gaskin; Alexander K Goel; Genevieve C Jacobs; David Kwan; Damien M Luviano; Morgan P McBee; Kelly Miller; Abdul Moiz Hafiz; Ceferino Obcemea; Anil V Parwani; Veronica Rotemberg; Elliot L Silver; Erik S Storm; James E Tcheng; Karen S Thullner; Les R Folio
Journal:  J Digit Imaging       Date:  2021-06-15       Impact factor: 4.056

Review 4.  Biomedical Ontologies to Guide AI Development in Radiology.

Authors:  Ross W Filice; Charles E Kahn
Journal:  J Digit Imaging       Date:  2021-11-01       Impact factor: 4.903

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.