Standard Lexicons, Coding Systems and Ontologies for Interoperability and Semantic Computation in Imaging

Abstract

Standard clinical terms, codes, and ontologies promote clarity and interoperability. Within radiology, there is a variety of relevant content resources, tools and technologies. These provide the basis for fundamental imaging workflows such as reporting and billing, and also facilitate a range of applications in quality improvement and research. This article reviews the key characteristics of lexicons, coding systems, and ontologies. A number of standards are described, including International Classification of Diseases-10-Clinical Modification (ICD-10-CM), Current Procedural Terminology (CPT), Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT), Logical Observation Identifiers Names and Codes (LOINC), and RadLex. Tools for accessing this material are reviewed, such as the National Center for Biomedical Ontology BioPortal system. Web services are discussed as a mechanism for semantic application development. Several example systems, workflows, and research applications using semantic technology are also surveyed.

RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning

“…RIL-Contour supports voxel annotations to define regions of interest (ROI) within images and text annotations to describe location-independent features or observations (e.g., image quality, presence of features, comments). RIL-Contour supports associating voxel ROI annotations with RadLex identification numbers (RadLex ID) to enable ROI definitions to be associated with a universally identifiable numerical nomenclature [22]. All dataset annotation operations are saved automatically as they are performed.…”

“…Many annotation tools create annotations on an ad hoc basis. These tools place the burden of maintaining consistency in annotation labels on the analyst and have inspired efforts to standardize annotation lexicon [22]. Errors or variability in data annotation increases the size of the dataset required for deep-learning model convergence to a “correct” generalizable solution [23].…”

Integrating an Ontology of Radiology Differential Diagnosis with ICD-10-CM, RadLex, and SNOMED CT

“…The International Classification of Diseases, version 10, Clinical Modification (ICD-10-CM) is an adaptation of the World Health Organization disease coding scheme that is used in the USA as a source of diagnostic codes. ICD-10-CM codes typically are used to encode the indications for a radiology exam, and thus are incorporated into the patient's clinical records and billing information [4][5][6]. RadLex is an ontology of radiology terms developed and published by the Radiological Society of North America (RSNA) [6][7][8].…”

“…ICD-10-CM codes typically are used to encode the indications for a radiology exam, and thus are incorporated into the patient's clinical records and billing information [4][5][6]. RadLex is an ontology of radiology terms developed and published by the Radiological Society of North America (RSNA) [6][7][8]. It was developed to address gaps in general clinical vocabularies [9], and incorporates terms for anatomy, diseases, and imaging signs [10].…”

“…RadLex has been used to index radiological literature, learning materials, and radiological procedures. The Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) is the world's most comprehensive clinical healthcare terminology; it enables consistent representation of clinical content in electronic health records and has been used widely in biomedical information systems [6,11].…”