AIMC Topic: Corneal Topography
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A Hybrid Deep Learning Construct for Detecting Keratoconus From Corneal Maps.
Translational vision science & technology
Dec 1, 2021
PURPOSE: To develop and assess the accuracy of a hybrid deep learning construct for detecting keratoconus (KCN) based on corneal topographic maps.
Screening Candidates for Refractive Surgery With Corneal Tomographic-Based Deep Learning.
JAMA ophthalmology
May 1, 2020
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...
[Assistant diagnose for subclinical keratoconus by artificial intelligence].
[Zhonghua yan ke za zhi] Chinese journal of ophthalmology
Dec 11, 2019
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 ...
[Application of Deep Learning in Early Diagnosis Assistant System of Keratoconus].
Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
Mar 30, 2019
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...
[A machine learning model on orthokeratology lens fitting based on the data of optometry examination].
[Zhonghua yan ke za zhi] Chinese journal of ophthalmology
Feb 11, 2019
To get an orthokeratology lens fitting model according to the research of the optometry examination data, which can help to improve the work efficiency and increase the hitting rate of prescription. The relationship between the basic optometry exam...
Predicting Refractive Surgery Outcome: Machine Learning Approach With Big Data.
Journal of refractive surgery (Thorofare, N.J. : 1995)
Sep 1, 2017
PURPOSE: To develop a decision forest for prediction of laser refractive surgery outcome.
Evaluation of a Machine-Learning Classifier for Keratoconus Detection Based on Scheimpflug Tomography.
Cornea
Jun 1, 2016
PURPOSE: To evaluate the performance of a support vector machine algorithm that automatically and objectively identifies corneal patterns based on a combination of 22 parameters obtained from Pentacam measurements and to compare this method with othe...
Accuracy of machine learning classifiers using bilateral data from a Scheimpflug camera for identifying eyes with preclinical signs of keratoconus.
Journal of cataract and refractive surgery
Feb 1, 2016
PURPOSE: To describe the topographic and tomographic characteristics of normal fellow eyes of unilateral keratoconus cases and to evaluate the accuracy of machine learning classifiers in discriminating healthy corneas from the normal fellow corneas.
Anatomical and visual outcome of intravitreal bevacizumab (Avastin) in patients with diabetic macular edema.
Nepalese journal of ophthalmology : a biannual peer-reviewed academic journal of the Nepal Ophthalmic Society : NEPJOPH
Jan 1, 2016
BACKGROUND: Intravitreal bevacizumab has been shown to be an effective treatment of diabetic macular edema.
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