McMaster University, 1280 Main Street West, Hamilton, Ontario, L8S 4L8, Canada.
Department of Dermatology, Sree Narayana Institute of Medical Sciences, Kunnukara, Kerala, India.
J Digit Imaging. 2022 Oct;35(5):1231-1237. doi: 10.1007/s10278-022-00636-5. Epub 2022 Apr 29.
Clinical images are vital for diagnosing and monitoring skin diseases, and their importance has increased with the growing popularity of machine learning. Lack of standards has stifled innovation in dermatological imaging, unlike other image-intensive specialties such as radiology. We investigate the meta-requirements for utilizing the popular DICOM standard for metadata management of images in dermatology. We propose practical design solutions and provide open-source tools to integrate dermatologists' workflow with enterprise imaging systems. Using the tool, dermatologists can tag, search, organize and convert clinical images to the DICOM format. We believe that our less disruptive approach will improve the adoption of standards in the specialty.
临床图像对于诊断和监测皮肤疾病至关重要,随着机器学习的日益普及,其重要性也在不断提高。与放射学等其他图像密集型专业不同,缺乏标准已经阻碍了皮肤科成像的创新。我们研究了在皮肤科中利用流行的 DICOM 标准进行元数据管理图像的元需求。我们提出了实用的设计解决方案,并提供了开源工具,将皮肤科医生的工作流程与企业成像系统集成在一起。使用该工具,皮肤科医生可以标记、搜索、组织和将临床图像转换为 DICOM 格式。我们相信,我们这种破坏性较小的方法将提高该专业对标准的采用。