Please use this identifier to cite or link to this item: https://doi.org/10.1097/APO.0000000000000404
Title: Deep Learning for Retinal Image Quality Assessment of Optic Nerve Head Disorders
Authors: Chan, Ebenezer Jia Jun
Najjar, Raymond P 
Tang, Zhiqun
Milea, Dan 
Keywords: Science & Technology
Life Sciences & Biomedicine
Ophthalmology
deep learning
optic nerve head
optic neuropathy
papilledema
retinal image quality assessment
ARTIFICIAL-INTELLIGENCE
FUNDUS
PAPILLEDEMA
BARRIERS
Issue Date: 1-May-2021
Publisher: ASIA-PACIFIC ACAD OPHTHALMOLOGY-APAO
Citation: Chan, Ebenezer Jia Jun, Najjar, Raymond P, Tang, Zhiqun, Milea, Dan (2021-05-01). Deep Learning for Retinal Image Quality Assessment of Optic Nerve Head Disorders. ASIA-PACIFIC JOURNAL OF OPHTHALMOLOGY 10 (3) : 282-288. ScholarBank@NUS Repository. https://doi.org/10.1097/APO.0000000000000404
Abstract: ABSTRACT: Deep learning (DL)-based retinal image quality assessment (RIQA) algorithms have been gaining popularity, as a solution to reduce the frequency of diagnostically unusable images. Most existing RIQA tools target retinal conditions, with a dearth of studies looking into RIQA models for optic nerve head (ONH) disorders. The recent success of DL systems in detecting ONH abnormalities on color fundus images prompts the development of tailored RIQA algorithms for these specific conditions. In this review, we discuss recent progress in DL-based RIQA models in general and the need for RIQA models tailored for ONH disorders. Finally, we propose suggestions for such models in the future.
Source Title: ASIA-PACIFIC JOURNAL OF OPHTHALMOLOGY
URI: https://scholarbank.nus.edu.sg/handle/10635/210600
ISSN: 21620989
DOI: 10.1097/APO.0000000000000404
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