scholarly journals Automatic Drusen Segmentation for Age-Related Macular Degeneration in Fundus Images Using Deep Learning

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1617
Author(s):  
Quang T. M. Pham ◽  
Sangil Ahn ◽  
Su Jeong Song ◽  
Jitae Shin
Keyword(s):  
Deep Learning ◽  
Macular Degeneration ◽  
Early Stage ◽  
Age Related Macular Degeneration ◽  
Fundus Images ◽  
Main Aspect ◽  
Multi Scale ◽  
Age Related ◽  
Segmentation Task ◽  
Deep Learning Model

Drusen are the main aspect of detecting age-related macular degeneration (AMD). Ophthalmologists can evaluate the condition of AMD based on drusen in fundus images. However, in the early stage of AMD, the drusen areas are usually small and vague. This leads to challenges in the drusen segmentation task. Moreover, due to the high-resolution fundus images, it is hard to accurately predict the drusen areas with deep learning models. In this paper, we propose a multi-scale deep learning model for drusen segmentation. By exploiting both local and global information, we can improve the performance, especially in the early stages of AMD cases.

scholarly journals Efficacy of Psychiatric Treatment to Treat a Specific Phobia of Intravitreal Injections in a Patient with Neovascular Age-Related Macular Degeneration

2021 ◽  
pp. 48-56
Author(s):  
Atsuta Ozaki ◽  
Hisashi Matsubara ◽  
Masahiko Sugimoto ◽  
Manami Kuze ◽  
Mineo Kondo ◽  
...  
Keyword(s):  
Macular Degeneration ◽  
Macular Edema ◽  
Psychiatric Treatment ◽  
Early Stage ◽  
Behavioral Therapy ◽  
Specific Phobia ◽  
Age Related Macular Degeneration ◽  
Intravitreal Injections ◽  
Anti Vegf ◽  
Age Related

Intravitreal injection of anti-vascular endothelial growth factor (anti-VEGF) is essential for the treatment of macular diseases such as wet age-related macular degeneration and macular edema. Although continued treatment is needed to maintain good vision, some patients cannot continue such injections for various reasons, including specific phobias. Here, we report a case of a patient with a specific phobia of intravitreal injections who could resume treatment after undergoing combined drug and cognitive-behavioral therapy (CBT). A 74-year-old Japanese man diagnosed with retinal angiomatous proliferation by fluorescein angiography and indocyanine green angiography was treated with intravitreal anti-VEGF injection. However, at 8 months after the first treatment, he became difficult to treat because of a phobia of injections. He was treated with photodynamic therapy, but his macular edema did not improve. After a psychiatric consultation, he was diagnosed with a specific phobia of intravitreal injections. Combined drug and CBT enabled him to resume receiving intravitreal injections. This case demonstrates that a specific phobia of intravitreal injections may benefit from combined drug and CBT. In this regard, some patients with high anxiety and fear of intravitreal injections should be referred to a psychiatrist at an early stage.

scholarly journals Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images

2014 ◽  
Vol 53 ◽  
pp. 55-64 ◽  
Author(s):  
Muthu Rama Krishnan Mookiah ◽  
U. Rajendra Acharya ◽  
Joel E.W. Koh ◽  
Vinod Chandran ◽  
Chua Kuang Chua ◽  
...  
Keyword(s):  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Fundus Images ◽  
Automated Diagnosis ◽  
Age Related

scholarly journals Improving Efficiency in Separating Blood Vessels from Retinal Images with Deep Learning Techniques

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3637-3640
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Macular Degeneration ◽  
Blood Vessels ◽  
Age Related Macular Degeneration ◽  
Retinal Images ◽  
Retinal Vessels ◽  
Age Related ◽  
Learning Techniques ◽  
Retinal Pathologies

Retinal vessels ID means to isolate the distinctive retinal configuration issues, either wide or restricted from fundus picture foundation, for example, optic circle, macula, and unusual sores. Retinal vessels recognizable proof investigations are drawing in increasingly more consideration today because of pivotal data contained in structure which is helpful for the identification and analysis of an assortment of retinal pathologies included yet not restricted to: Diabetic Retinopathy (DR), glaucoma, hypertension, and Age-related Macular Degeneration (AMD). With the advancement of right around two decades, the inventive methodologies applying PC supported systems for portioning retinal vessels winding up increasingly significant and coming nearer. Various kinds of retinal vessels segmentation strategies discussed by using Deep Learning methods. At that point, the pre-processing activities and the best in class strategies for retinal vessels distinguishing proof are presented.

scholarly journals Utility of Deep Learning Methods for Referability Classification of Age-Related Macular Degeneration

2018 ◽  
Vol 136 (11) ◽  
pp. 1305 ◽  
Author(s):  
Phillippe Burlina ◽  
Neil Joshi ◽  
Katia D. Pacheco ◽  
David E. Freund ◽  
Jun Kong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Learning Methods ◽  
Age Related

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

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 261
Author(s):  
Tae-Young Heo ◽  
Kyoung Min Kim ◽  
Hyun Kyu Min ◽  
Sun Mi Gu ◽  
Jae Hyun Kim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Deep Learning ◽  
Macular Degeneration ◽  
Data Augmentation ◽  
Treatment Strategies ◽  
Image Data ◽  
Age Related Macular Degeneration ◽  
Age Related ◽  
Leave One Out

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 methodology for precise treatment strategies and prognosis. Here, we developed deep learning algorithms and predicted diseases using 399 images of fundus. 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. Image-data augmentation in our model was performed using Keras ImageDataGenerator, and the leave-one-out procedure was used for model cross-validation. The prediction and validation results obtained using the AI AMD diagnosis model showed relevant performance and suitability as well as better diagnostic accuracy than manual review by first-year residents. These results suggest the efficacy of this tool for early differential diagnosis of AMD in situations involving shortages of ophthalmology specialists and other medical devices.

scholarly journals Development and validation of a novel prognostic model for predicting AMD progression using longitudinal fundus images

2020 ◽  
Vol 5 (1) ◽  
pp. e000569
Author(s):  
Joshua Bridge ◽  
Simon Harding ◽  
Yalin Zheng
Keyword(s):  
Deep Learning ◽  
Age Related Macular Degeneration ◽  
Superior Performance ◽  
Prognostic Models ◽  
Eye Disease ◽  
Final Decision ◽  
Fundus Images ◽  
Imaging Data ◽  
Age Related ◽  
Longitudinal Imaging

ObjectiveTo develop a prognostic tool to predict the progression of age-related eye disease progression using longitudinal colour fundus imaging.Methods and analysisPrevious prognostic models using deep learning with imaging data require annotation during training or only use a single time point. We propose a novel deep learning method to predict the progression of diseases using longitudinal imaging data with uneven time intervals, which requires no prior feature extraction. Given previous images from a patient, our method aims to predict whether the patient will progress onto the next stage of the disease. The proposed method uses InceptionV3 to produce feature vectors for each image. In order to account for uneven intervals, a novel interval scaling is proposed. Finally, a recurrent neural network is used to prognosticate the disease. We demonstrate our method on a longitudinal dataset of colour fundus images from 4903 eyes with age-related macular degeneration (AMD), taken from the Age-Related Eye Disease Study, to predict progression to late AMD.ResultsOur method attains a testing sensitivity of 0.878, a specificity of 0.887 and an area under the receiver operating characteristic of 0.950. We compare our method to previous methods, displaying superior performance in our model. Class activation maps display how the network reaches the final decision.ConclusionThe proposed method can be used to predict progression to advanced AMD at some future visit. Using multiple images at different time points improves predictive performance.

Epilutein for Early-Stage Age-Related Macular Degeneration: A Randomized and Prospective Study

2017 ◽  
Vol 58 (4) ◽  
pp. 231-241 ◽  
Author(s):  
Raimondo Forte ◽  
Lucia Panzella ◽  
Ida Cesarano ◽  
Gilda Cennamo ◽  
Thomas Eidenberger ◽  
...  
Keyword(s):  
Prospective Study ◽  
Macular Degeneration ◽  
Early Stage ◽  
Age Related Macular Degeneration ◽  
Age Related
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