Using Deep Learning in Automated Detection of Graft Detachment in Descemet Membrane Endothelial Keratoplasty: A Pilot Study.

Journal: Cornea

PMID: 30325845

Abstract

PURPOSE: To evaluate a deep learning-based method to automatically detect graft detachment (GD) after Descemet membrane endothelial keratoplasty (DMEK) in anterior segment optical coherence tomography (AS-OCT).

Authors

Maximilian Treder

Department of Ophthalmology, University of Muenster Medical Center, Domagkstraße 15, 48149, Muenster, Germany. maximilian.treder@ukmuenster.de.
Jost Lennart Lauermann

Department of Ophthalmology, University of Muenster Medical Center, Domagkstraße 15, 48149, Muenster, Germany.
Maged Alnawaiseh
Nicole Eter

Department of Ophthalmology, University of Muenster Medical Center, Domagkstraße 15, 48149, Muenster, Germany.

Keywords

Aged Deep Learning Descemet Membrane Descemet Stripping Endothelial Keratoplasty Endothelium, Corneal Female Graft Rejection Humans Male Middle Aged Neural Networks, Computer Pilot Projects Sensitivity and Specificity Tomography, Optical Coherence

External Resources

View on PubMed Access via DOI PubMed (30325845)

Using Deep Learning in Automated Detection of Graft Detachment in Descemet Membrane Endothelial Keratoplasty: A Pilot Study.

Abstract

Authors

Keywords

External Resources

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