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CC BY V4.0, Open Access
Journal article
Automated detection and classification of early AMD biomarkers using deep learning
Scientific reports, Vol.9(1), 10990
2019
PMCID: PMC6662691
PMID: 31358808
Abstract
Age-related macular degeneration (AMD) affects millions of people and is a leading cause of blindness throughout the world. Ideally, affected individuals would be identified at an early stage before late sequelae such as outer retinal atrophy or exudative neovascular membranes develop, which could produce irreversible visual loss. Early identification could allow patients to be staged and appropriate monitoring intervals to be established. Accurate staging of earlier AMD stages could also facilitate the development of new preventative therapeutics. However, accurate and precise staging of AMD, particularly using newer optical coherence tomography (OCT)-based biomarkers may be time-intensive and requires expert training which may not feasible in many circumstances, particularly in screening settings. In this work we develop deep learning method for automated detection and classification of early AMD OCT biomarker. Deep convolution neural networks (CNN) were explicitly trained for performing automated detection and classification of hyperreflective foci, hyporeflective foci within the drusen, and subretinal drusenoid deposits from OCT B-scans. Numerous experiments were conducted to evaluate the performance of several state-of-the-art CNNs and different transfer learning protocols on an image dataset containing approximately 20000 OCT B-scans from 153 patients. An overall accuracy of 87% for identifying the presence of early AMD biomarkers was achieved.
Details
- Title
- Automated detection and classification of early AMD biomarkers using deep learning
- Authors/Creators
- Sajib Saha - Commonwealth Scientific and Industrial Research OrganisationMarco Nassisi - Doheny Eye InstituteMo Wang - Doheny Eye InstituteSophiana Lindenberg - Doheny Eye InstituteYogi Kanagasingam - Australian e-Health Research CentreSrinivas Sadda - Doheny Eye InstituteZhihong Jewel Hu - Doheny Eye Institute
- Publication Details
- Scientific reports, Vol.9(1), 10990
- Publisher
- Springer Nature
- Number of pages
- 9
- Grant note
- Macula Vision Research Foundation (MVRF) Australia Endeavour Scholarships and Fellowships R21EY030619 / NATIONAL EYE INSTITUTE; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Eye Institute (NEI) R21-EY030619 / NIH-NEI; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Eye Institute (NEI) BrightFocus Foundation Macular Degeneration program; BrightFocus Foundation
- Identifiers
- 991005579417907891
- Copyright
- © The Author(s) 2019
- Murdoch Affiliation
- Centre for Biosecurity and One Health
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 1 Clinical & Life Sciences
- 1.36 Ophthalmology
- 1.36.212 Genetic Retinopathies
- Web Of Science research areas
- Ophthalmology
- ESI research areas
- Clinical Medicine