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 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 Artificial intelligence and deep learning in ophthalmology

2018 ◽  
Vol 103 (2) ◽  
pp. 167-175 ◽  
Author(s):  
Daniel Shu Wei Ting ◽  
Louis R Pasquale ◽  
Lily Peng ◽  
John Peter Campbell ◽  
Aaron Y Lee ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Language Processing ◽  
Visual Fields ◽  
Classification Performance ◽  
Age Related Macular Degeneration ◽  
Patient Acceptance ◽  
Age Related ◽  
Ocular Imaging ◽  
Fundus Photographs

Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI ‘black-box’ algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward.

Applications of Artificial Intelligence for the Diagnosis, Prognosis, and Treatment of Age-related Macular Degeneration

2020 ◽  
Vol 60 (4) ◽  
pp. 147-168
Author(s):  
Louis Cai ◽  
John W. Hinkle ◽  
Diego Arias ◽  
Richard J. Gorniak ◽  
Paras C. Lakhani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Age Related

scholarly journals Management of Neovascular Age-Related Macular Degeneration in Clinical Practice: Initiation, Maintenance, and Discontinuation of Therapy

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Pearse A. Keane ◽  
Adnan Tufail ◽  
Praveen J. Patel
Keyword(s):  
Clinical Practice ◽  
Macular Degeneration ◽  
Treatment Strategies ◽  
Age Related Macular Degeneration ◽  
Vascular Endothelial ◽  
Future Directions ◽  
Age Related ◽  
Endothelial Growth Factor ◽  
Current Guidelines ◽  
Review Current

Neovascular age-related macular degeneration (AMD) is a leading cause of irreversible visual loss in elderly populations. In recent years, pharmacological inhibition of vascular endothelial growth factor (VEGF), via intravitreal injection of ranibizumab (Lucentis) or bevacizumab (Avastin), has offered the first opportunity to improve visual outcomes in patients diagnosed with this disorder. In this paper, we provide recommendations on how bevacizumab and ranibizumab may be best applied in current clinical practice, with an emphasis on their underlying pharmacology and efficacy. In addition, we review current guidelines for the initiation, maintenance, and discontinuation of anti-VEGF therapies, as well as emerging treatment strategies and future directions in the field.

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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