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Corneal Topography

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[Application of Deep Learning in Early Diagnosis Assistant System of Keratoconus].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
In view of the problem that there is no standard diagnosis for early stage keratoconus disease,at the same time to assist the special examiner and ophthalmologist to make the early diagnosis effectively,the advantages and disadvantages of each testin...

Computer aided diagnosis for suspect keratoconus detection.

Computers in biology and medicine
PURPOSE: To develop a stable and low-cost computer aided diagnosis (CAD) system for early keratoconus detection for clinical use.

[Assistant diagnose for subclinical keratoconus by artificial intelligence].

[Zhonghua yan ke za zhi] Chinese journal of ophthalmology
To investigate the diagnosis of normal cornea, subclinical keratoconus and keratoconus by artifical intelligence. Diagnostic study. From January 2016 to January 2019, who admitted to Tianjin Eye Hospital from 18 to 48 years old, with an average of ...

The Role of Corneal Biomechanics for the Evaluation of Ectasia Patients.

International journal of environmental research and public health
PURPOSE: To review the role of corneal biomechanics for the clinical evaluation of patients with ectatic corneal diseases.

Screening Candidates for Refractive Surgery With Corneal Tomographic-Based Deep Learning.

JAMA ophthalmology
IMPORTANCE: Evaluating corneal morphologic characteristics with corneal tomographic scans before refractive surgery is necessary to exclude patients with at-risk corneas and keratoconus. In previous studies, researchers performed screening with machi...

Corneal Topography Raw Data Classification Using a Convolutional Neural Network.

American journal of ophthalmology
PURPOSE: We investigated the efficiency of a convolutional neural network applied to corneal topography raw data to classify examinations of 3 categories: normal, keratoconus (KC), and history of refractive surgery (RS).

Keratoconus Screening Based on Deep Learning Approach of Corneal Topography.

Translational vision science & technology
PURPOSE: To develop and compare deep learning (DL) algorithms to detect keratoconus on the basis of corneal topography and validate with visualization methods.

Unsupervised learning for large-scale corneal topography clustering.

Scientific reports
Machine learning algorithms have recently shown their precision and potential in many different use cases and fields of medicine. Most of the algorithms used are supervised and need a large quantity of labeled data to achieve high accuracy. Also, mos...

Artificial Intelligence Efficiently Identifies Regional Differences in the Progression of Tomographic Parameters of Keratoconic Corneas.

Journal of refractive surgery (Thorofare, N.J. : 1995)
PURPOSE: To develop an artificial intelligence (AI) model to effectively assess local versus global progression of keratoconus using multiple tomographic parameters.

Artificial intelligence applications in different imaging modalities for corneal topography.

Survey of ophthalmology
Interpretation of topographical maps used to detect corneal ectasias requires a high level of expertise. Several artificial intelligence (AI) technologies have attempted to interpret topographic maps. The purpose of this study is to provide a review ...