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.

scholarly journals Quantitative Imaging Biomarkers in Age-Related Macular Degeneration and Diabetic Eye Disease: A Step Closer to Precision Medicine

2021 ◽  
Vol 11 (11) ◽  
pp. 1161
Author(s):  
Gagan Kalra ◽  
Sudeshna Sil Kar ◽  
Duriye Damla Sevgi ◽  
Anant Madabhushi ◽  
Sunil K. Srivastava ◽  
...  
Keyword(s):  
Deep Learning ◽  
Macular Degeneration ◽  
Precision Medicine ◽  
Biomarker Discovery ◽  
Age Related Macular Degeneration ◽  
Imaging Biomarkers ◽  
Patient Specific ◽  
Eye Disease ◽  
Age Related ◽  
Diabetic Eye Disease

The management of retinal diseases relies heavily on digital imaging data, including optical coherence tomography (OCT) and fluorescein angiography (FA). Targeted feature extraction and the objective quantification of features provide important opportunities in biomarker discovery, disease burden assessment, and predicting treatment response. Additional important advantages include increased objectivity in interpretation, longitudinal tracking, and ability to incorporate computational models to create automated diagnostic and clinical decision support systems. Advances in computational technology, including deep learning and radiomics, open new doors for developing an imaging phenotype that may provide in-depth personalized disease characterization and enhance opportunities in precision medicine. In this review, we summarize current quantitative and radiomic imaging biomarkers described in the literature for age-related macular degeneration and diabetic eye disease using imaging modalities such as OCT, FA, and OCT angiography (OCTA). Various approaches used to identify and extract these biomarkers that utilize artificial intelligence and deep learning are also summarized in this review. These quantifiable biomarkers and automated approaches have unleashed new frontiers of personalized medicine where treatments are tailored, based on patient-specific longitudinally trackable biomarkers, and response monitoring can be achieved with a high degree of accuracy.

scholarly journals A New EYENET Model for Diagnosis of Age-Related Macular Degeneration

Ophthalmology ◽  
2018 ◽  
pp. 153-171
Author(s):  
Priya Kandan ◽  
P. Aruna
Keyword(s):  
Macular Degeneration ◽  
Experimental Results ◽  
Age Related Macular Degeneration ◽  
Disease Spread ◽  
Eye Disease ◽  
Fundus Images ◽  
Central Vision ◽  
Age Related

Age-related macular degeneration is an eye disease, that gradually degrades the macula, a part of the retina, which is responsible for central vision. It occurs in one of the two types, DRY and WET age-related macular degeneration. In this chapter, to diagnose Age-related macular degeneration, the authors have proposed a new EYENET model which was obtained by combining the modified PNN and modified RBFNN and hence it poses the advantages of both the models. The amount of the disease spread in the retina can be identified by extracting the features of the retina. A total of 250 fundus images were used, out of which 150 were used for training and 100 images were used for testing. Experimental results show that PNN has an accuracy of 87%, modified PNN has an accuracy of 90% RBFNN has an accuracy of 80%, modified RBFNN has an accuracy of 85% and the proposed EYENET Model has an accuracy of 94%. This infers that the proposed EYENET model outperforms all other models.

A New EYENET Model for Diagnosis of Age-Related Macular Degeneration

2016 ◽  
pp. 422-440
Author(s):  
Priya Kandan ◽  
P. Aruna
Keyword(s):  
Macular Degeneration ◽  
Experimental Results ◽  
Age Related Macular Degeneration ◽  
Disease Spread ◽  
Eye Disease ◽  
Fundus Images ◽  
Central Vision ◽  
Age Related

Age-related macular degeneration is an eye disease, that gradually degrades the macula, a part of the retina, which is responsible for central vision. It occurs in one of the two types, DRY and WET age-related macular degeneration. In this chapter, to diagnose Age-related macular degeneration, the authors have proposed a new EYENET model which was obtained by combining the modified PNN and modified RBFNN and hence it poses the advantages of both the models. The amount of the disease spread in the retina can be identified by extracting the features of the retina. A total of 250 fundus images were used, out of which 150 were used for training and 100 images were used for testing. Experimental results show that PNN has an accuracy of 87%, modified PNN has an accuracy of 90% RBFNN has an accuracy of 80%, modified RBFNN has an accuracy of 85% and the proposed EYENET Model has an accuracy of 94%. This infers that the proposed EYENET model outperforms all other models.

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.

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