Radiology teaching file repositories contain a large amount of information about patient health and radiologist interpretation of medical findings. Although valuable for radiology education, the use of teaching file repositories has been hindered by the ability to perform advanced searches on these repositories given the unstructured format of the data and the sparseness of the different repositories. Our term coverage analysis of two major medical ontologies, Radiology Lexicon (RadLex) and Unified Medical Language System (UMLS) Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), and two teaching file repositories, Medical Imaging Resource Community (MIRC) and MyPacs, showed that both ontologies combined cover 56.3% of terms in the MIRC and only 17.9% of terms in MyPacs. Furthermore, the overlap between the two ontologies (i.e., terms included by both the RadLex and UMLS SNOMED CT) was a mere 5.6% for the MIRC and 2% for the RadLex. Clustering the content of the teaching file repositories showed that they focus on different diagnostic areas within radiology. The MIRC teaching file covers mostly pediatric cases; a few cases are female patients with heart-, chest-, and bone-related diseases. The MyPacs contains a range of different diseases with no focus on a particular disease category, gender, or age group. MyPacs also provides a wide variety of cases related to the neck, face, heart, chest, and breast. These findings provide valuable insights on what new cases should be added or how existent cases may be integrated to provide more comprehensive data repositories. Similarly, the low-term coverage by the ontologies shows the need to expand ontologies with new terminology such as new terms learned from these teaching file repositories and validated by experts. While our methodology to organize and index data using clustering approaches and medical ontologies is applied to teaching file repositories, it can be applied to any other medical clinical data.
Keywords: Cluster analysis; Coverage analysis; Data integration; Medical ontologies; Radiology teaching files; Unsupervised machine learning.