Publication
Title
Keratoconus diagnosis : from fundamentals to artificial intelligence: a systematic narrative review
Author
Abstract
The remarkable recent advances in managing keratoconus, the most common corneal ectasia, encouraged researchers to conduct further studies on the disease. Despite the abundance of information about keratoconus, debates persist regarding the detection of mild cases. Early detection plays a crucial role in facilitating less invasive treatments. This review encompasses corneal data ranging from the basic sciences to the application of artificial intelligence in keratoconus patients. Diagnostic systems utilize automated decision trees, support vector machines, and various types of neural networks, incorporating input from various corneal imaging equipment. Although the integration of artificial intelligence techniques into corneal imaging devices may take time, their popularity in clinical practice is increasing. Most of the studies reviewed herein demonstrate a high discriminatory power between normal and keratoconus cases, with a relatively lower discriminatory power for subclinical keratoconus.
Language
English
Source (journal)
Diagnostics
Publication
Basel : Mdpi , 2023
ISSN
2075-4418
DOI
10.3390/DIAGNOSTICS13162715
Volume/pages
13 :16 (2023) , p. 1-29
Article Reference
2715
ISI
001055597600001
Pubmed ID
37627975
Medium
E-only publicatie
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 02.10.2023
Last edited 24.05.2024
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