Automated semantic indexing of figure captions to improve radiology image retrieval.
OBJECTIVE
We explored automated concept-based indexing of unstructured figure captions to improve retrieval of images from radiology journals.


METHODS
The MetaMap Transfer program (MMTx) was used to map the text of 84,846 figure captions from 9,004 peer-reviewed, English-language articles to concepts in three controlled vocabularies from the UMLS Metathesaurus, version 2006AA. Sampling procedures were used to estimate the standard information-retrieval metrics of precision and recall, and to evaluate the degree to which concept-based retrieval improved image retrieval.


METHODS
Precision was estimated based on a sample of 250 concepts. Recall was estimated based on a sample of 40 concepts. The authors measured the impact of concept-based retrieval to improve upon keyword-based retrieval in a random sample of 10,000 search queries issued by users of a radiology image search engine.


RESULTS
Estimated precision was 0.897 (95% confidence interval, 0.857-0.937). Estimated recall was 0.930 (95% confidence interval, 0.838-1.000). In 5,535 of 10,000 search queries (55%), concept-based retrieval found results not identified by simple keyword matching; in 2,086 searches (21%), more than 75% of the results were found by concept-based search alone.


CONCLUSIONS
Concept-based indexing of radiology journal figure captions achieved very high precision and recall, and significantly improved image retrieval.
Kahn
Charles E
CE
Division of Informatics, Department of Radiology, Medical College of Wisconsin, 9200 W. Wisconsin Ave., Milwaukee, WI 53226, USA. kahn@mcw.edu
Rubin
Daniel L
DL
D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D016001:Confidence Intervals; D016247:Information Storage and Retrieval; D008488:Medical Illustration; D010506:Periodicals as Topic; D011871:Radiology; D012660:Semantics; D017432:Unified Medical Language System