Deep Learning for Retinal Image Quality Assessment of Optic Nerve Head Disorders. Issue 3 (May 2021)

• Record Type: Journal Article
• Authors: Chan, Ebenezer Jia Jun; Najjar, Raymond P.; Tang, Zhiqun; Milea, Dan
• Is Part Of: Asia-Pacific journal of ophthalmology. Volume 10:Issue 3(2021)
• Publication Date: 2021-05
• Subjects: deep learning -- optic nerve head -- optic neuropathy -- papilledema -- retinal image quality assessment; Ophthalmology -- Periodicals; Eye -- Diseases -- Periodicals; Periodicals; 617.7005
• DOI: 10.1097/APO.0000000000000404
• Languages: English
• ISSNs: 0129-1653

Abstract

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. Abstract : Supplemental Digital Content is available in the text