Evidence map›Paper›PMID 35766023›Full record

ArticleThe Journal of international medical research2022

Correlation between corneal dynamic responses and keratoconus topographic parameters.

Hsi-Yun Tai, Jun-Ji Lin, Yi-Hung Huang, Po-Jen Shih, I-Jong Wang, Jia-Yush Yen

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Article in The Journal of international medical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
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

6 authors.

Hsi-Yun TaiDepartment of Mechanical Engineering, National Taiwan University, Taipei.
Jun-Ji LinDepartment of Mechanical Engineering, National Taiwan University, Taipei.
Yi-Hung HuangDepartment of Mechanical Engineering, National Taiwan University, Taipei.
Po-Jen ShihDepartment of Biomedical Engineering, National Taiwan University, Taipei.
I-Jong WangDepartment of Ophthalmology, National Taiwan University Hospital, Taipei.
Jia-Yush YenDepartment of Mechanical Engineering, National Taiwan University, Taipei.ORCID https://orcid.org/0000-0001-8795-9211

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo investigate the correlation between corneal biomechanical properties and topographic parameters using machine learning networks for automatic severity diagnosis and reference benchmark construction.

methodsThis was a retrospective study involving 31 eyes from 31 patients with keratonus. Two clustering approaches were used (i.e., shape-based and feature-based). The shape-based method used a keratoconus benchmark validated for indicating the severity of keratoconus. The feature-based method extracted imperative features for clustering analysis.

resultsThere were strong correlations between the symmetric modes and the keratoconus severity and between the asymmetric modes and the location of the weak centroid. The Pearson product-moment correlation coefficient (PPMC) between the symmetric mode and normality was 0.92 and between the asymmetric mode and the weak centroid value was 0.75.

conclusionThis study confirmed that there is a relationship between the keratoconus signs obtained from topography and the corneal dynamic behaviour captured by the Corvis ST device. Further studies are required to gather more patient data to establish a more extensive database for validation.

Indexed as

KeratoconusCluster AnalysisCorneaCorneal TopographyHumansRetrospective StudiesCorneal dynamic characteristicsCorvis STkeratoconusmachine learning

Identifiers

PMID35766023
PMCPMC9247384

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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.