Automated Identification of Clinically Relevant Regions in Glaucoma OCT Reports Using Expert Eye Tracking Data and Deep Learning

Tian, Y., Sharma, A., Mehta, S., Kaushal, S., Liebmann, J. M., Cioffi, G. A., & Thakoor, K. A. (2024). Automated Identification of Clinically Relevant Regions in Glaucoma OCT Reports Using Expert Eye Tracking Data and Deep Learning. Translational Vision Science & Technology, 13(10), 24. https://doi.org/10.1167/tvst.13.10.24
Authors:
Ye Tian
Anurag Sharma
Shubh Mehta
Shubham Kaushal
Jeffrey M Liebmann
George A Cioffi
Kaveri A Thakoor
Affiliated Authors:
Ye Tian
Anurag Sharma
Shubh Mehta
Shubham Kaushal
Jeffrey M Liebmann
George A Cioffi
Kaveri A Thakoor
Columbia Affiliation:
Subjects:
Detection and Management of Retinal Diseases (OpenAlex Topic)
Global Prevalence and Treatment of Glaucoma (OpenAlex Topic)
Deep Learning in Medical Image Analysis (OpenAlex Topic)
Deep Learning (MeSH)
Tomography, Optical Coherence (MeSH)
Glaucoma (MeSH)
Eye-Tracking Technology (MeSH)
Author Keywords:
convolutional neural networks
glaucoma
optical coherence tomography
u-net
eye tracking
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Publication Type:
Article
Unique ID:
10.1167/tvst.13.10.24
PMID:
39405074
DOI:
10.1167/tvst.13.10.24
Journal:
Translational Vision Science and Technology
Publication Date:
Data Source:
PubMed
Source Link:
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Record Created: