Corneal Topography Raw Data Classification Using a Convolutional Neural Network.

Journal: American journal of ophthalmology

PMID: 32533948

Abstract

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).

Authors

Pierre Zéboulon

Department of Ophthalmology, Rothschild Foundation, Paris, France. Electronic address: pierrezeboulon@gmail.com.
Guillaume Debellemanière

Department of Ophthalmology, Rothschild Foundation, Paris, France.
Magalie Bouvet

Department of Ophthalmology, Rothschild Foundation, Paris, France.
Damien Gatinel

Department of Ophthalmology, Rothschild Foundation, Paris, France; CEROC (Center of Expertise and Research in Optics for Clinicians), Paris, France.

Keywords

Adult Corneal Pachymetry Corneal Topography Female Healthy Volunteers Humans Keratoconus Machine Learning Male Middle Aged Neural Networks, Computer Refractive Errors Refractive Surgical Procedures Reproducibility of Results Retrospective Studies Sensitivity and Specificity

External Resources

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Corneal Topography Raw Data Classification Using a Convolutional Neural Network.

Abstract

Authors

Keywords

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