scholarly journals Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration

2016 ◽  
Author(s):  
Cecilia S Lee ◽  
Doug M Baughman ◽  
Aaron Y Lee
Keyword(s):  
Deep Learning ◽  
Macular Degeneration ◽  
Imaging Modality ◽  
Age Related Macular Degeneration ◽  
Automated Image Analysis ◽  
Clinical Endpoints ◽  
Patient Level ◽  
Age Related ◽  
Peak Sensitivity ◽  
Automated Screening

Objective: The advent of Electronic Medical Records (EMR) with large electronic imaging databases along with advances in deep neural networks with machine learning has provided a unique opportunity to achieve milestones in automated image analysis. Optical coherence tomography (OCT) is the most commonly obtained imaging modality in ophthalmology and represents a dense and rich dataset when combined with labels derived from the EMR. 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). Design: EMR and OCT database study Subjects: Normal and AMD patients who had a macular OCT. Methods: Automated extraction of an OCT imaging database was performed and linked to clinical endpoints from the EMR. OCT macula scans were obtained by Heidelberg Spectralis, and each OCT scan was linked to EMR clinical endpoints extracted from EPIC. The central 11 images were selected from each OCT scan of two cohorts of patients: normal and AMD. Cross-validation was performed using a random subset of patients. Receiver operator curves (ROC) were constructed at an independent image level, macular OCT level, and patient level. Main outcome measure: Area under the ROC. Results: Of a recent extraction of 2.6 million OCT images linked to clinical datapoints from the EMR, 52,690 normal macular OCT images and 48,312 AMD macular OCT images were selected. 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%. At the macula level, we achieved an area under the ROC of 93.83% with an accuracy of 88.98%. At a patient level, we achieved an area under the ROC of 97.45% with an accuracy of 93.45%. Peak sensitivity and specificity with optimal cutoffs were 92.64% and 93.69% respectively. Conclusions: Deep learning techniques achieve high accuracy and is effective as a new image classification technique. These findings have important implications in utilizing OCT in automated screening and the development of computer aided diagnosis tools in the future.

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 Combined automated screening for age-related macular degeneration and diabetic retinopathy in primary care settings

2021 ◽  
Vol 6 ◽  
pp. 12-12
Author(s):  
Alauddin Bhuiyan ◽  
Arun Govindaiah ◽  
Sharmina Alauddin ◽  
Oscar Otero-Marquez ◽  
R. Theodore Smith
Keyword(s):  
Primary Care ◽  
Diabetic Retinopathy ◽  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Primary Care Settings ◽  
Age Related ◽  
Automated Screening

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 Optical Coherence Tomographic Angiography Imaging in Age-Related Macular Degeneration

2017 ◽  
Vol 9 ◽  
pp. 117917211668607 ◽  
Author(s):  
Jeffrey Ma ◽  
Ria Desai ◽  
Peter Nesper ◽  
Manjot Gill ◽  
Amani Fawzi ◽  
...  
Keyword(s):  
Macular Degeneration ◽  
Imaging Modality ◽  
Noninvasive Imaging ◽  
Age Related Macular Degeneration ◽  
Optical Coherence ◽  
Age Related ◽  
Flow Information ◽  
Noninvasive Imaging Modality ◽  
Choroidal Vasculature ◽  
Tomographic Angiography

Optical coherence tomographic angiography (OCTA) is emerging as a rapid, noninvasive imaging modality that can provide detailed structural and flow information on retinal and choroidal vasculature. This review contains an introduction of OCTA and summarizes the studies to date on OCTA imaging in age-related macular degeneration.

scholarly journals Automated Summarisation of SDOCT Volumes using Deep Learning: Transfer Learning vs de novo Trained Networks

2018 ◽  
Author(s):  
Bhavna J. Antony ◽  
Stefan Maetschke ◽  
Rahil Garnavi
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
De Novo ◽  
Imaging Modality ◽  
Age Related Macular Degeneration ◽  
Learning Approaches ◽  
Non Invasive ◽  
Age Related ◽  
Volumetric Images ◽  
Small Set

AbstractSpectral-domain optical coherence tomography (SDOCT) is a non-invasive imaging modality that generates high-resolution volumetric images. This modality finds widespread usage in ophthalmology for the diagnosis and management of various ocular conditions. The volumes generated can contain 200 or more B-scans. Manual inspection of such large quantity of scans is time consuming and error prone in most clinical settings. Here, we present a method for the generation of visual summaries of SDOCT volumes, wherein a small set of B-scans that highlight the most clinically relevant features in a volume are extracted. The method was trained and evaluated on data acquired from age-related macular degeneration patients, and “relevance” was defined as the presence of visibly discernible structural abnormalities. The summarisation system consists of a detection module, where relevant B-scans are extracted from the volume, and a set of rules that determines which B-scans are included in the visual summary. Two deep learning approaches are presented and compared for the classification of B-scans - transfer learning and de novo learning. Both approaches performed comparably with AUCs of 0.97 and 0.96, respectively, obtained on an independent test set. The de novo network, however, was 98% smaller than the transfer learning approach, and had a run-time that was also significantly shorter.

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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