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Article . 2023
License: CC BY NC
Data sources: ZENODO
ZENODO
Article . 2023
License: CC BY NC
Data sources: Datacite
ZENODO
Article . 2023
License: CC BY NC
Data sources: Datacite
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Detection of Subclinical Keratoconus using Machine Learning Algorithms

Authors: Palla Hemanth; T. Sravan Kumar; Sk. Nawaz Shareef; S. Preethi; B. Siva Prasad;

Detection of Subclinical Keratoconus using Machine Learning Algorithms

Abstract

A non-inflammatory corneal condition called keratoconus frequently affects both eyes. Due to corneal deformation and scarring, the bilateral ecstatic illness known as keratoconus can impair vision. The prevalence of keratoconus varies from 1 in 375 people in Northern Europe to as high as 1 in 48 in various ethnic groups. Studies indicate a higher incidence and faster advancement in Middle-Eastern, West Indian, and Asian populations. The disease normally starts after puberty and progresses over the following two to three decades at a varied rate. As the condition worsens, corneal distortion may become so severe that patients will no longer be able to see well enough to function without the use of soft or hard contact lenses. Contact lenses are not always well-tolerated, and vision loss can significantly lower quality of life. About 20% of patients are given the option of a corneal transplant during the course of the disease to help them see better, but doing so carries the risk of postoperative complications including microbial keratitis and inflammation, probable allograft rejection, and transplant failure. The majority of people with keratoconus are found to have visual disturbances or an increase in refractive astigmatism.

Keywords

Probable allograft rejection, astigmatism., Microbial keratitis and inflammation, Contact lenses, Transplant failure

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average