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Development of a Deep-Learning-Based Artificial Intelligence Tool for Differential Diagnosis between Dry and Neovascular Age-Related Macular Degeneration

Tae-Young Heo, Kyoung Min Kim, Hyun Kyu Min, Sun Mi Gu, Jae Hyun Kim, Jaesuk Yun, Jung Kee Min
2020 Diagnostics  
The use of deep-learning-based artificial intelligence (AI) is emerging in ophthalmology, with AI-mediated differential diagnosis of neovascular age-related macular degeneration (AMD) and dry AMD a promising  ...  Based on feature extraction and classification with fully connected layers, we applied the Visual Geometry Group with 16 layers (VGG16) model of convolutional neural networks to classify new images.  ...  CAM visualization of normal, dry age-related macular degeneration (dAMD), and neovascular age-related macular degeneration (nAMD) retinas.  ... 
doi:10.3390/diagnostics10050261 pmid:32354098 fatcat:ostfnqnh5vhplgsj22tc6nqsfi

Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks

Philippe M. Burlina, Neil Joshi, Michael Pekala, Katia D. Pacheco, David E. Freund, Neil M. Bressler
2017 JAMA ophthalmology  
Question When applying deep learning methods to the automated assessment of fundus images, what is the accuracy for detecting age-related macular degeneration?  ...  DESIGN, SETTING, AND PARTICIPANTS Deep convolutional neural networks that are explicitly trained for performing automated AMD grading were compared with an alternate deep learning method that used transfer  ...  It used a stochastic gradient descent with a Nesterov momentum, with an initial learning rate that was set to Abbreviations: AREDS, Age-Related Eye Disease Study; DCNN, deep convolutional neural networks  ... 
doi:10.1001/jamaophthalmol.2017.3782 pmid:28973096 pmcid:PMC5710387 fatcat:ra7qdfm7mjaujjaqhhxpqj2w4e

Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration [article]

Cecilia S. Lee, Doug M. Baughman, Aaron Y. Lee
2016 arXiv   pre-print
We sought to determine if deep learning could be utilized to distinguish normal OCT images from images from patients with Age-related Macular Degeneration (AMD).  ...  A deep neural network was trained to categorize images as either normal or AMD. At the image level, we achieved an auROC of 92.78% with an accuracy of 87.63%.  ...  respectively in the US Medicare population. 1 Since its development in 1991, 2 a 70-fold increase in OCT use for diagnosing age-related macular degeneration (AMD) was reported between 2002 and 2009.  ... 
arXiv:1612.04891v1 fatcat:zhg7pyapkzhepcqegliioxrb4m

Index [chapter]

2021 State of the Art in Neural Networks and their Applications  
117, 155 for age-related macular degeneration detection and grading, 33À36 for automated segmentation of kidney, 186 for cataract detection and grading, 43À45 for diabetic retinopathy detection and  ...  , 2 Ocular diseases with CNN deep learning networks age-related macular degeneration, 31À36 cataract, 42À45 diabetic retinopathy, 36À42 glaucoma, 25À31 screening and diagnosis of, 25À45 Ocular surface  ... 
doi:10.1016/b978-0-12-819740-0.00023-1 fatcat:dz4hretj2fbh5cmevwejvz56zy

Non-exudative Age-related Macular Degeneration [chapter]

Neelakshi Bhagat, Christina Flaxel
2007 Age-Related Macular Degeneration, Second Edition  
Among deep-learning methods, convolution neural networks (CNNs) show superior image recognition ability.  ...  The use of optical coherence tomography (OCT) images is increasing in the medical treatment of age-related macular degeneration (AMD), and thus, the amount of data requiring analysis is increasing.  ...  CNN convolution neural network, AMD age-related macular degeneration, AUROC area under the receiver operating characteristic curve Authorship.  ... 
doi:10.3109/9781420019865-7 fatcat:kg4chr2zfjbdpmq2efuxr5ofbq

Clinical Wide-Field Retinal Image Deep Learning Classification of Exudative and Non-Exudative Age-Related Macular Degeneration

Nathaniel Tak, Akshay J Reddy, Juliette Martel, James B Martel
2021 Cureus  
Age-related macular degeneration (AMD) is a disease that currently affects approximately 196 million individuals and is projected to affect 288 million in 2040.  ...  A deep neural network, specifically a convoluted neural network (CNN) can be trained to differentiate between different types of AMD images given the proper training data.  ...  Convoluted neural networks (CNNs) composed of convolutional layers are primarily used for visual applications. This can be a form of a deep neural network as there are often times many hidden layers.  ... 
doi:10.7759/cureus.17579 pmid:34646633 pmcid:PMC8480936 fatcat:24sfwowlpzaohmwcv2a2bvycwa

A Comparison of Handcrafted and Deep Neural Network Feature Extraction for Classifying Optical Coherence Tomography (OCT) Images [article]

Kuntoro Adi Nugroho
2018 arXiv   pre-print
The proposed study aims to compare the effectiveness of handcrafted and deep neural network features.  ...  The deep neural network based methods also demonstrated better result on the under represented class.  ...  The OCT images belong to four class, namely Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen (presents in eary Age-Related Macular Degeneration), and Normal.  ... 
arXiv:1809.03306v1 fatcat:vaaiks7eqjegdilonjq753nezy

Fundus Image Analysis for Age Related Macular Degeneration: ADAM-2020 Challenge Report [article]

Sharath M Shankaranarayana
2020 arXiv   pre-print
Age related macular degeneration (AMD) is one of the major causes for blindness in the elderly population.  ...  We propose the use of generative adversarial networks (GANs) for the tasks of segmentation and detection. We also propose a novel method of fovea detection using GANs.  ...  Acknowledgement We thank the organizers of the Automatic Detection challenge on Age-related Macular degeneration (ADAM) ( for hosting the challenge and kindly providing  ... 
arXiv:2009.01548v1 fatcat:dwknxv6xtjbu3mj2w5svlyqheq

Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration [article]

Cecilia S Lee, Doug M Baughman, Aaron Y Lee
2016 bioRxiv   pre-print
We sought to determine if deep learning could be utilized to distinguish normal OCT images from images from patients with Age-related Macular Degeneration (AMD).  ...  A deep neural network was trained to categorize images as either normal or AMD. At the image level, we achieved an area under the ROC of 92.78% with an accuracy of 87.63%.  ...  version of the VGG16 convolutional neural network 23 was used as the deep learning 120 model for classification (Figure 1).  ... 
doi:10.1101/094276 fatcat:w7xk34xwlvbjtgr2j2n6b4losa

Deep Learning based Retinal OCT Segmentation [article]

Mike Pekala, Neil Joshi, David E. Freund, Neil M. Bressler, Delia Cabrera DeBuc, Philippe M Burlina
2018 arXiv   pre-print
The proposed automated approach segments images using fully convolutional networks (FCNs) together with Gaussian process (GP)-based regression as a post-processing step to improve the quality of the estimates  ...  Our objective is to evaluate the efficacy of methods that use deep learning (DL) for the automatic fine-grained segmentation of optical coherence tomography (OCT) images of the retina.  ...  Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophtalmology, . .  ... 
arXiv:1801.09749v1 fatcat:474syzsyp5cxfljj646ecbtq6m

Accuracy of a deep convolutional neural network in the detection of myopic macular diseases using swept-source optical coherence tomography

Takahiro Sogawa, Hitoshi Tabuchi, Daisuke Nagasato, Hiroki Masumoto, Yasushi Ikuno, Hideharu Ohsugi, Naofumi Ishitobi, Yoshinori Mitamura
2020 PLoS ONE  
This study examined and compared outcomes of deep learning (DL) in identifying swept-source optical coherence tomography (OCT) images without myopic macular lesions [i.e., no high myopia (nHM) vs. high  ...  swept-source OCT images, the DL model was able to classify OCT images without myopic macular lesions and OCT images with myopic macular lesions such as mCNV and RS with high accuracy.  ...  Accuracy of ultra-wide-field fundus ophthalmoscopy-assisted deep learning, a machine-learning technology, for detecting age related macular degeneration.  ... 
doi:10.1371/journal.pone.0227240 pmid:32298265 pmcid:PMC7161961 fatcat:ylpsobwnujav3ft5pxyp3gnkwe

Automated age-related macular degeneration area estimation – first results [article]

Rokas Pečiulis and Mantas Lukoševičius and Algimantas Kriščiukaitis and Robertas Petrolis and Dovilė Buteikienė
2021 arXiv   pre-print
This work aims to research an automatic method for detecting Age-related Macular Degeneration (AMD) lesions in RGB eye fundus images.  ...  Using the data, we train and test five different convolutional neural networks: a custom one to classify healthy and AMD-affected eye fundi, and four well-known networks: ResNet50, ResNet101, MobileNetV3  ...  Introduction The current prevalence of early Age-related Macular Degeneration (AMD) in Europe is 3.5 % in those aged 55-59 years and 17.6 % in those aged >85 years.  ... 
arXiv:2107.02211v1 fatcat:36wepc36qbcofh7diqz7lakenu

Optical coherence tomography image for automatic classification of diabetic macular edema

Ping Wang, Jia-Li Li, Hao Ding
2020 Optica Applicata  
Features of the DME are automatically identified and extracted by the pre-trained convolutional neural network (CNN), which only involves fine-tuning the VGGNet-16 network without any user intervention  ...  Diabetic macular edema (DME) is the dominant reason of diabetic visual loss, so early detection and treatment of DME is of great significance for the treatment of diabetes.  ...  traction, retinal anterior membrane, drusen and other eye diseases, such as age-related macular degeneration.  ... 
doi:10.37190/oa200405 fatcat:mynw6ogssvenfprxyynjx5xlrq

Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images

Soichiro Kuwayama, Yuji Ayatsuka, Daisuke Yanagisono, Takaki Uta, Hideaki Usui, Aki Kato, Noriaki Takase, Yuichiro Ogura, Tsutomu Yasukawa
2019 Journal of Ophthalmology  
The remaining 100 images were used to evaluate the trained convolutional neural network (CNN) model. Results.  ...  The diagnoses involved normal eyes (n=570) and those with wet age-related macular degeneration (AMD) (n=136), diabetic retinopathy (DR) (n=104), epiretinal membranes (ERMs) (n=90), and another 19 diseases  ...  Deep learning is one of the machine learning techniques with a multilayered convolutional neural network (CNN) model to learn and detect image features [8] . Gulshan et al.  ... 
doi:10.1155/2019/6319581 pmid:31093370 pmcid:PMC6481014 fatcat:qeyikvecabfc3ese2m724k5apm

Review on the Role of Macular Edema in Retinopathy, Blindness and Automated Diagnosis Methods

M. Barman, D. Deb, M. Hassan, B. Choudhury
2021 EAI Endorsed Transactions on Pervasive Health and Technology  
INTRODUCTION: Macular edema is not a disease itself, but, a very common condition in most of the retinal diseases, such as diabetic retinopathy, retinal vein occlusion, hypertensive retinopathy, age-related  ...  We have analyzed vast state-of-the-art methods for retinal abnormality detection.  ...  Retinal Vein Occlusion Age-Related Macular Degeneration Age-related Macular Degeneration (AMD) is the leading cause of permanent vision loss among people aged over 60.  ... 
doi:10.4108/eai.17-3-2021.169034 fatcat:uxbftisi3zhlhejpdtjkz76oqm
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