Proof-of-Concept Analysis of a Deep Learning Model to Conduct Automated Segmentation of OCT Images for Macular Hole Volume.

Journal: Ophthalmic surgery, lasers & imaging retina

Published Date: Apr 1, 2022

Abstract

BACKGROUND AND OBJECTIVE: To determine whether an automated artificial intelligence (AI) model could assess macular hole (MH) volume on swept-source optical coherence tomography (OCT) images.

Authors

Austin Pereira

Department of Ophthalmology & Visual Sciences, University of Toronto, Toronto, ON Canada.
Jonathan D Oakley

Voxeleron LLC, Pleasanton, California, United States.
Simrat K Sodhi

School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom.
Daniel B Russakoff

Voxeleron LLC, Pleasanton, California, United States.
Netan Choudhry

Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.

Keywords

Artificial Intelligence Deep Learning Humans Retinal Perforations Retrospective Studies Tomography, Optical Coherence Vitrectomy

External Resources

View on PubMed Access via DOI PubMed (35417293)

Proof-of-Concept Analysis of a Deep Learning Model to Conduct Automated Segmentation of OCT Images for Macular Hole Volume.

Abstract

Authors

Keywords

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