Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review.

ABSTRACT:

Background

Keratoconus is a disorder characterized by progressive thinning and distortion of the cornea. If detected at an early stage, corneal collagen cross-linking can prevent disease progression and further visual loss. Although advanced forms are easily detected, reliable identification of subclinical disease can be problematic. Several different machine learning algorithms have been used to improve the detection of subclinical keratoconus based on the analysis of multiple types of clinical measures, such as corneal imaging, aberrometry, or biomechanical measurements.

Objective

The aim of this study is to survey and critically evaluate the literature on the algorithmic detection of subclinical keratoconus and equivalent definitions.

Methods

For this systematic re

SUBMITTER: Maile H 

PROVIDER: S-EPMC8713097 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature