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Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study

التفاصيل البيبلوغرافية
العنوان: Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study
المؤلفون: Ehsan Vaghefi, Sophie Hill, Hannah M. Kersten, David Squirrell
المصدر: Journal of Ophthalmology, Vol 2020 (2020)
بيانات النشر: Hindawi Limited, 2020.
سنة النشر: 2020
المجموعة: LCC:Ophthalmology
مصطلحات موضوعية: Ophthalmology, RE1-994
الوصف: Background and Objective. To determine if using a multi-input deep learning approach in the image analysis of optical coherence tomography (OCT), OCT angiography (OCT-A), and colour fundus photographs increases the accuracy of a CNN to diagnose intermediate dry age-related macular degeneration (AMD). Patients and Methods. Seventy-five participants were recruited and divided into three cohorts: young healthy (YH), old healthy (OH), and patients with intermediate dry AMD. Colour fundus photography, OCT, and OCT-A scans were performed. The convolutional neural network (CNN) was trained on multiple image modalities at the same time. Results. The CNN trained using OCT alone showed a diagnostic accuracy of 94%, whilst the OCT-A trained CNN resulted in an accuracy of 91%. When multiple modalities were combined, the CNN accuracy increased to 96% in the AMD cohort. Conclusions. Here we demonstrate that superior diagnostic accuracy can be achieved when deep learning is combined with multimodal image analysis.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2090-004X
2090-0058
العلاقة: https://doaj.org/toc/2090-004XTest; https://doaj.org/toc/2090-0058Test
DOI: 10.1155/2020/7493419
الوصول الحر: https://doaj.org/article/d00283bdf4424bafa68210e4b35701deTest
رقم الانضمام: edsdoj.00283bdf4424bafa68210e4b35701de
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:2090004X
20900058
DOI:10.1155/2020/7493419