Evidence map›Paper›PMID 32752014›Full record

ReviewSensors (Basel, Switzerland)2020

Biometric Measurement of Anterior Segment: A Review.

Bin Liu, Chengwei Kang, Fengzhou Fang

Open access · goldAbstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
0.8field-weighted citation impact, top 31% 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

12 citing papers in PubMed, 14 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. The impact of type 2 diabetes mellitus on anterior segment parameters.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological Society
    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

3 authors at 1 institution in 2 countries.

Bin LiuTianjin Key Laboratory for Control Theory & Applications in Complicated Systems, Tianjin University of Technology, Tianjin 300384, China.ORCID 0000-0003-2543-4051
Chengwei KangCentre of Micro/Nano manufacturing Technology (MNMT-Dublin), University College Dublin, D04 V1W8 Dublin 4, Dublin, Ireland.
Fengzhou FangCentre of Micro/Nano manufacturing Technology (MNMT-Dublin), University College Dublin, D04 V1W8 Dublin 4, Dublin, Ireland.ORCID 0000-0002-8716-5988
University College Dublin · IE

Funding

National Natural Science Foundation of China 61975151Science Foundation Ireland (SFI) 15/RP/B3208State Administration of Foreign Experts Affairs and the Ministry of Education of China B07014
6 · The paper itself

Abstract

Biometric measurement of the anterior segment is of great importance for the ophthalmology, human eye modeling, contact lens fitting, intraocular lens design, etc. This paper serves as a comprehensive review on the historical development and basic principles of the technologies for measuring the geometric profiles of the anterior segment. Both the advantages and drawbacks of the current technologies are illustrated. For in vivo measurement of the anterior segment, there are two main challenges that need to be addressed to achieve high speed, fine resolution, and large range imaging. One is the motion artefacts caused by the inevitable and random human eye movement. The other is the serious multiple scattering effects in intraocular turbid media. The future research perspectives are also outlined in this paper.

Indexed as

BiometryAnterior Eye SegmentHumansTomography, Optical Coherenceanterior segmentcorneal topographygeometric measurementtomography

Identifiers

PMID32752014
PMCPMC7435894
OpenAlexW3046230743

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.