English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Fundus photography (FP) remains the primary imaging modality in screeningvarious retinal diseases including age-related macular degeneration, diabeticretinopathy and glaucoma. FP allows the clinician to examine the ocular fundusstructures such as the macula, the optic disc (OD) and retinal vessels, whosevisibility and clarity in an FP image remain central to ensuring diagnosticaccuracy, and hence determine the diagnostic quality (DQ). Images with low DQ,resulting from eye movement, improper illumination and other possible causes,should obviously be recaptured. However, the technician, often unfamiliar withDQ criteria, initiates recapture only based on expert feedback. The processpotentially engages the imaging device multiple times for single subject, andwastes the time and effort of the ophthalmologist, the technician and thesubject. The burden could be prohibitive in case of teleophthalmology, whereobtaining feedback from the remote expert entails additional communication costand delay. Accordingly, a strong need for automated diagnostic qualityassessment (DQA) has been felt, where an image is immediately assigned a DQcategory. In response, motivated by the notional continuum of DQ, we propose ahierarchical deep learning (DL) architecture to distinguish between good,usable and unusable categories. On the public EyeQ dataset, we achieve anaccuracy of 89.44%, improving upon existing methods. In addition, usinggradient based class activation map (Grad-CAM), we generate a visualexplanation which agrees with the expert intuition. Future FP cameras equippedwith the proposed DQA algorithm will potentially improve the efficacy of theteleophthalmology as well as the traditional system.

Diagnostic Quality Assessment of Fundus Photographs: Hierarchical Deep Learning with Clinically Significant Explanations