Cram Dawn, Eid Monief, Goldburgh Mitchell M, Nagels Jason, Yudkovitch Lawrence, Towbin Alexander J
PaxeraHealth, 85 Wells Street, Newton, MA, 02459, USA.
Ministry of Health, Riyadh, Saudi Arabia.
J Imaging Inform Med. 2024 Dec;37(6):2709-2721. doi: 10.1007/s10278-024-01118-6. Epub 2024 Jun 10.
Previously, the lack of a standard body part ontology has been identified as a critical deficiency needed to enable enterprise imaging. This whitepaper aims to provide a comprehensive assessment of anatomical ontologies with the aim of facilitating enterprise imaging. It offers an overview of the process undertaken by the Health Information Management Systems Society (HIMSS) and Society for Imaging Informatics in medicine (SIIM) Enterprise Imaging Community Data Standards Evaluation workgroup to assess the viability of existing ontologies for supporting cross-disciplinary medical imaging workflows. The report analyzes the responses received from representatives of three significant ontologies: SNOMED CT, LOINC, and ICD, and delves into their suitability for the complex landscape of enterprise imaging. It highlights the strengths and limitations of each ontology, ultimately concluding that SNOMED CT is the most viable solution for standardizing anatomy terminology across the medical imaging community.
此前，缺乏标准的身体部位本体已被确定为实现企业成像所需的关键缺陷。本白皮书旨在对解剖学本体进行全面评估，以促进企业成像。它概述了健康信息管理系统协会（HIMSS）和医学影像信息学协会（SIIM）企业成像社区数据标准评估工作组为评估现有本体支持跨学科医学成像工作流程的可行性而开展的过程。该报告分析了从三个重要本体（SNOMED CT、LOINC和ICD）的代表那里收到的回复，并深入探讨了它们在企业成像复杂环境中的适用性。它突出了每个本体的优势和局限性，最终得出结论，SNOMED CT是跨医学成像社区标准化解剖学术语的最可行解决方案。
