scholarly journals Accuracy of ultra-wide-field fundus ophthalmoscopy-assisted deep learning, a machine-learning technology, for detecting age-related macular degeneration

2018 ◽  
Vol 39 (6) ◽  
pp. 1269-1275 ◽  
Author(s):  
Shinji Matsuba ◽  
Hitoshi Tabuchi ◽  
Hideharu Ohsugi ◽  
Hiroki Enno ◽  
Naofumi Ishitobi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Wide Field ◽  
Learning Technology ◽  
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

Assessment of CFH and HTRA1 polymorphisms in age-related macular degeneration using classic and machine-learning approaches

2020 ◽  
Vol 41 (6) ◽  
pp. 539-547
Author(s):  
Antonieta Martínez-Velasco ◽  
Andric C. Perez-Ortiz ◽  
Bani Antonio-Aguirre ◽  
Lourdes Martínez-Villaseñor ◽  
Esmeralda Lira-Romero ◽  
...  
Keyword(s):  
Machine Learning ◽  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Learning Approaches ◽  
Age Related

scholarly journals The Relevance of Cataract as a Risk Factor for Age-Related Macular Degeneration: A Machine Learning Approach

2019 ◽  
Vol 9 (24) ◽  
pp. 5550
Author(s):  
Antonieta Martínez-Velasco ◽  
Lourdes Martínez-Villaseñor ◽  
Luis Miralles-Pechuán ◽  
Andric C. Perez-Ortiz ◽  
Juan C. Zenteno ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Macular Degeneration ◽  
Significant Risk ◽  
Age Related Macular Degeneration ◽  
Supervised Machine Learning ◽  
Current Debate ◽  
Learning Approach ◽  
Age Related ◽  
Machine Learning Approach

Age-related macular degeneration (AMD) is the leading cause of visual dysfunction and irreversible blindness in developed countries and a rising cause in underdeveloped countries. There is a current debate on whether or not cataracts are significant risk factors for AMD development. In particular, research regarding this association is so far inconclusive. For this reason, we aimed to employ here a machine-learning approach to analyze the relevance and importance of cataracts as a risk factor for AMD in a large cohort of Hispanics from Mexico. We conducted a nested case control study of 119 cataract cases and 137 healthy unmatched controls focusing on clinical data from electronic medical records. Additionally, we studied two single nucleotide polymorphisms in the CFH gene previously associated with the disease in various populations as positive control for our method. We next determined the most relevant variables and found the bivariate association between cataracts and AMD. Later, we used supervised machine-learning methods to replicate these findings without bias. To improve the interpretability, we detected the five most relevant features and displayed them using a bar graph and a rule-based tree. Our findings suggest that bilateral cataracts are not a significant risk factor for AMD development among Hispanics from Mexico.

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 A Systematic Review of Deep Learning Methods Applied to Ocular Images

2019 ◽  
Vol 30 (1) ◽  
pp. 9-26 ◽  
Author(s):  
Oscar Julian Perdomo Charry ◽  
Fabio Augusto González Osorio
Keyword(s):  
Deep Learning ◽  
Macular Degeneration ◽  
Clinical Signs ◽  
Age Related Macular Degeneration ◽  
Ocular Diseases ◽  
Learning Methods ◽  
Age Related ◽  
Box Models ◽  
The One

Artificial intelligence is having an important effect on different areas of medicine, and ophthalmology has not been the exception. In particular, deep learning methods have been applied successfully to the detection of clinical signs and the classification of ocular diseases. This represents a great potential to increase the number of people correctly diagnosed. In ophthalmology, deep learning methods have primarily been applied to eye fundus images and optical coherence tomography. On the one hand, these methods have achieved an outstanding performance in the detection of ocular diseases such as: diabetic retinopathy, glaucoma, diabetic macular degeneration and age-related macular degeneration.  On the other hand, several worldwide challenges have shared big eye imaging datasets with segmentation of part of the eyes, clinical signs and the ocular diagnostic performed by experts. In addition, these methods are breaking the stigma of black-box models, with the delivering of interpretable clinically information. This review provides an overview of the state-of-the-art deep learning methods used in ophthalmic images, databases and potential challenges for ocular diagnosis

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