Utility of Deep Learning Methods for Referability Classification of Age-Related Macular Degeneration

Phillippe Burlina; Neil Joshi; Katia D Pacheco; David E Freund; Jun Kong; Neil M Bressler

doi:10.1001/jamaophthalmol.2018.3799

Utility of Deep Learning Methods for Referability Classification of Age-Related Macular Degeneration

JAMA Ophthalmol. 2018 Nov 1;136(11):1305-1307. doi: 10.1001/jamaophthalmol.2018.3799.

Authors

Phillippe Burlina^{1

2

3}, Neil Joshi¹, Katia D Pacheco⁴, David E Freund¹, Jun Kong⁵, Neil M Bressler^{2

6}

Affiliations

¹ Applied Physics Laboratory, Johns Hopkins University, Baltimore, Maryland.
² Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland.
³ Department of Computer Science, Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland.
⁴ Retina Division, Department of Ophthalmology, Brazilian Center of Vision Eye Hospital, Brasilia, Brazil.
⁵ Department of Ophthalmology, The Fourth Affiliated Hospital of China Medical University, Eye Hospital of China Medical University, Shenyang, China.
⁶ Editor.

Abstract

This study uses fundus images from a national data set to assess 2 deep learning methods for referability classification of age-related macular degeneration.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Aged
Deep Learning*
Diagnosis, Computer-Assisted / methods*
False Positive Reactions
Humans
Macular Degeneration / classification*
Neural Networks, Computer
Ophthalmologists / standards
Predictive Value of Tests
Reproducibility of Results
Sensitivity and Specificity

Grants and funding

R21 EY024310/EY/NEI NIH HHS/United States