ArticleJournal of digital imaging2008
Effective metadata discovery for dynamic filtering of queries to a radiology image search engine.
Article in Journal of digital imaging, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Accurate determination of imaging modality using an ensemble of text- and image-based classifiers.Journal of digital imaging · 2012Trial
- Caption-based topical descriptors for microscopic images as published in academic papers.Health information and libraries journal · 2010Article
- Automated semantic indexing of figure captions to improve radiology image retrieval.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We sought to demonstrate the effectiveness of techniques to index radiology images using metadata discovered in their free-text figure captions. The ARRS GoldMiner image library incorporated 94,256 figures from 11,712 articles published in peer-reviewed online radiology journals. Algorithms were developed to discover metadata--age, sex, and imaging modality--from the figures' free-text captions. Age was recorded in years, and was classified as infant (less than 2 years), child (2 to 17 years), or adult (18+ years). Each figure was assigned to one of eight imaging modalities. A random sample of 1,000 images was examined to measure accuracy of the metadata. The patient's age was identified in 58,994 cases (63%), and the patient's sex was identified in 58,427 cases (62%). An imaging modality was assigned to 80,402 (85%) of the figures. Based on the 1,000 sampled cases, recall values for age, sex, and imaging modality were 97.2%, 99.7%, and 86.4%, respectively. Precision values for age, sex, and imaging modality were 100%, 100%, and 97.2%, respectively. Automated techniques can accurately discover age, sex, and imaging modality metadata from captions of figures published in radiology journals. The metadata can be used to dynamically filter queries for an image search engine.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.