ReviewLasers in medical science2009
Towards automated classification of clinical optical coherence tomography data of dense tissues.
Review in Lasers in medical science, 2009. 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.
- Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 1: feature extraction.Journal of biomedical optics · 2013Article
- New X-ray imaging modalities and their integration with intravascular imaging and interventions.The international journal of cardiovascular imaging · 2010Review
- Quantitative tool for rapid disease mapping using optical coherence tomography images of azoxymethane-treated mouse colon.Journal of biomedical opticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The native contrast of optical coherence tomography (OCT) data in dense tissues can pose a challenge for clinical decision making. Automated data evaluation is one way of enhancing the clinical utility of measurements. Methods for extracting information from structural OCT data are appraised here. A-scan analysis allows characterization of layer thickness and scattering parameters, whereas image analysis renders itself to segmentation, texture and speckle analysis. All fully automated approaches combine pre-processing, feature registration, data reduction, and classification. Pre-processing requires de-noising, feature recognition, normalization and refining. In the current literature, image exclusion criteria, initial parameters, or manual input are common requirements. The interest of the presented methods lies in the prospect of objective, quick, and/or post-acquisition processing. There is a potential to improve clinical decision making based on automated processing of OCT data.
Indexed as
Identifiers
18936871What 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.