Evidence map›Paper›PMID 25406740›Full record

ArticleBiomedical engineering online2014

Automatic method of analysis and measurement of additional parameters of corneal deformation in the Corvis tonometer.

Robert Koprowski

Open access · goldAbstract read
In one paragraph

Article in Biomedical engineering online, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed
6.2field-weighted citation impact, top 3% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 72 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Observational
  8. Article
  9. Review
  10. Observational
  11. Article
  12. Biomechanical properties measured with dynamic Scheimpflug analyzer in central serous chorioretinopathy.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Patient-specific air puff-induced loading using machine learning.Frontiers in bioengineering and biotechnology · 2023
    Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Robert KoprowskiDepartment of Biomedical Computer Systems, University of Silesia, Faculty of Computer Science and Materials Science, Institute of Computer Science, ul, Będzińska 39, Sosnowiec 41-200, Poland. robert.koprowski@us.edu.pl.
University of Silesia in Katowice · PL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe method for measuring intraocular pressure using the Corvis tonometer provides a sequence of images of corneal deformation. Deformations of the cornea are recorded using the ultra-high-speed Scheimpflug camera. This paper presents a new and reproducible method of analysis of corneal deformation images that allows for automatic measurements of new features, namely new three parameters unavailable in the original software. MATERIAL AND

methodThe images subjected to processing had a resolution of 200 × 576 × 140 pixels. They were acquired from the Corvis tonometer and simulation. In total 14,000 2D images were analysed. The image analysis method proposed by the author automatically detects the edge of the cornea and sclera fragments. For this purpose, new methods of image analysis and processing proposed by the author as well as those well-known, such as Canny filter, binarization, median filtering etc., have been used. The presented algorithms were implemented in Matlab (version 7.11.0.584-R2010b) with Image Processing toolbox (version 7.1-R2010b) using both known algorithms for image analysis and processing and those proposed by the author.

resultsOwing to the proposed algorithm it is possible to determine three parameters: (1) the degree of the corneal reaction relative to the static position; (2) the corneal length changes; (3) the ratio of amplitude changes to the corneal deformation length. The corneal reaction is smaller by about 30.40% compared to its static position. The change in the corneal length during deformation is very small, approximately 1% of its original length. Parameter (3) enables to determine the applanation points with a correlation of 92% compared to the conventional method for calculating corneal flattening areas. The proposed algorithm provides reproducible results fully automatically within a few seconds/per patient using Core i7 processor.

conclusionsUsing the proposed algorithm, it is possible to measure new, additional parameters of corneal deformation, which are not available in the original software. The presented analysis method provides three new parameters of the corneal reaction. Detailed clinical studies based on this method will be presented in subsequent papers.

Indexed as

Intraocular PressureTonometry, OcularAlgorithmsAutomationBiomechanical PhenomenaComputer SimulationCorneaHumansImage Processing, Computer-AssistedReproducibility of ResultsSoftware

Identifiers

PMID25406740
PMCPMC4258810
OpenAlexW2144693515

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.