scholarly journals Development and validation of a deep‐learning algorithm for the detection of neovascular age‐related macular degeneration from colour fundus photographs

2019 ◽  
Vol 47 (8) ◽  
pp. 1009-1018 ◽  
Author(s):  
Stuart Keel ◽  
Zhixi Li ◽  
Jane Scheetz ◽  
Liubov Robman ◽  
James Phung ◽  
...  
Keyword(s):  
Deep Learning ◽  
Macular Degeneration ◽  
Learning Algorithm ◽  
Age Related Macular Degeneration ◽  
Deep Learning Algorithm ◽  
Age Related ◽  
Fundus Photographs ◽  
Development And Validation

scholarly journals Labelling of data on fundus color pictures used to train a deep learning model enhances its macular pathology recognition capabilities

2021 ◽  
Author(s):  
KhP Takhchidi ◽  
PV Gliznitsa ◽  
SN Svetozarskiy ◽  
AI Bursov ◽  
KA Shusterzon
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Image Data ◽  
Diagnostic Methods ◽  
Age Related Macular Degeneration ◽  
Screening Programs ◽  
Deep Learning Algorithm ◽  
Age Related ◽  
Fundus Photographs

Retinal diseases remain one of the leading causes of visual impairments in the world. The development of automated diagnostic methods can improve the efficiency and availability of the macular pathology mass screening programs. The objective of this work was to develop and validate deep learning algorithms detecting macular pathology (age-related macular degeneration, AMD) based on the analysis of color fundus photographs with and without data labeling. We used 1200 color fundus photographs from local databases, including 575 retinal images of AMD patients and 625 pictures of the retina of healthy people. The deep learning algorithm was deployed in the Faster RCNN neural network with ResNet50 for convolution. The process employed the transfer learning method. As a result, in the absence of labeling, the accuracy of the model was unsatisfactory (79%) because the neural network selected the areas of attention incorrectly. Data labeling improved the efficacy of the developed method: with the test dataset, the model determined the areas with informative features adequately, and the classification accuracy reached 96.6%. Thus, image data labeling significantly improves the accuracy of retinal color images recognition by a neural network and enables development and training of effective models with limited datasets.

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

2020 ◽  
Vol 2020 ◽  
pp. 1-7 ◽  
Author(s):  
Ehsan Vaghefi ◽  
Sophie Hill ◽  
Hannah M. Kersten ◽  
David Squirrell
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Fundus Photography ◽  
Multiple Modalities ◽  
Age Related ◽  
Dry Amd ◽  
Fundus Photographs

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.

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

JAMA ◽  
2016 ◽  
Vol 316 (22) ◽  
pp. 2402 ◽  
Author(s):  
Varun Gulshan ◽  
Lily Peng ◽  
Marc Coram ◽  
Martin C. Stumpe ◽  
Derek Wu ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Deep Learning Algorithm ◽  
Fundus Photographs ◽  
Development And Validation ◽  
Retinal Fundus
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