scholarly journals Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ling-Ping Cen ◽  
Jie Ji ◽  
Jian-Wei Lin ◽  
Si-Tong Ju ◽  
Hong-Jie Lin ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Efficiency ◽  
Characteristic Curve ◽  
Weighted Average ◽  
Age Related Macular Degeneration ◽  
Learning Platform ◽  
Age Related ◽  
Public Data ◽  
Primary Test ◽  
Retinal Fundus

AbstractRetinal fundus diseases can lead to irreversible visual impairment without timely diagnoses and appropriate treatments. Single disease-based deep learning algorithms had been developed for the detection of diabetic retinopathy, age-related macular degeneration, and glaucoma. Here, we developed a deep learning platform (DLP) capable of detecting multiple common referable fundus diseases and conditions (39 classes) by using 249,620 fundus images marked with 275,543 labels from heterogenous sources. Our DLP achieved a frequency-weighted average F1 score of 0.923, sensitivity of 0.978, specificity of 0.996 and area under the receiver operating characteristic curve (AUC) of 0.9984 for multi-label classification in the primary test dataset and reached the average level of retina specialists. External multihospital test, public data test and tele-reading application also showed high efficiency for multiple retinal diseases and conditions detection. These results indicate that our DLP can be applied for retinal fundus disease triage, especially in remote areas around the world.

scholarly journals Deep learning and ensemble stacking technique for differentiating polypoidal choroidal vasculopathy from neovascular age-related macular degeneration

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yu-Bai Chou ◽  
Chung-Hsuan Hsu ◽  
Wei-Shiang Chen ◽  
Shih-Jen Chen ◽  
De-Kuang Hwang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Macular Degeneration ◽  
Polypoidal Choroidal Vasculopathy ◽  
Multiple Correspondence Analysis ◽  
Characteristic Curve ◽  
Medical Engineering ◽  
Age Related Macular Degeneration ◽  
Age Related ◽  
Sensitivity Specificity ◽  
Choroidal Vasculopathy

AbstractPolypoidal choroidal vasculopathy (PCV) and neovascular age-related macular degeneration (nAMD) share some similarity in clinical imaging manifestations. However, their disease entity and treatment strategy as well as visual outcomes are very different. To distinguish these two vision-threatening diseases is somewhat challenging but necessary. In this study, we propose a new artificial intelligence model using an ensemble stacking technique, which combines a color fundus photograph-based deep learning (DL) model and optical coherence tomography-based biomarkers, for differentiation of PCV from nAMD. Furthermore, we introduced multiple correspondence analysis, a method of transforming categorical data into principal components, to handle the dichotomous data for combining with another image DL system. This model achieved a robust performance with an accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of 83.67%, 80.76%, 84.72%, and 88.57%, respectively, by training nearly 700 active cases with suitable imaging quality and transfer learning architecture. This work could offer an alternative method of developing a multimodal DL model, improve its efficiency for distinguishing different diseases, and facilitate the broad application of medical engineering in a DL model design.

Updates in deep learning research in ophthalmology

2021 ◽  
Vol 135 (20) ◽  
pp. 2357-2376
Author(s):  
Wei Yan Ng ◽  
Shihao Zhang ◽  
Zhaoran Wang ◽  
Charles Jit Teng Ong ◽  
Dinesh V. Gunasekeran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Critical Role ◽  
Disease Classification ◽  
Age Related Macular Degeneration ◽  
Security And Privacy ◽  
Generative Adversarial Networks ◽  
End User ◽  
Ethical Assessment ◽  
Age Related ◽  
Patient Consent

Abstract Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical field. Deep learning (DL), in particular, has garnered significant attention due to the availability of large amounts of data and digitized ocular images. Currently, AI in Ophthalmology is mainly focused on improving disease classification and supporting decision-making when treating ophthalmic diseases such as diabetic retinopathy, age-related macular degeneration (AMD), glaucoma and retinopathy of prematurity (ROP). However, most of the DL systems (DLSs) developed thus far remain in the research stage and only a handful are able to achieve clinical translation. This phenomenon is due to a combination of factors including concerns over security and privacy, poor generalizability, trust and explainability issues, unfavorable end-user perceptions and uncertain economic value. Overcoming this challenge would require a combination approach. Firstly, emerging techniques such as federated learning (FL), generative adversarial networks (GANs), autonomous AI and blockchain will be playing an increasingly critical role to enhance privacy, collaboration and DLS performance. Next, compliance to reporting and regulatory guidelines, such as CONSORT-AI and STARD-AI, will be required to in order to improve transparency, minimize abuse and ensure reproducibility. Thirdly, frameworks will be required to obtain patient consent, perform ethical assessment and evaluate end-user perception. Lastly, proper health economic assessment (HEA) must be performed to provide financial visibility during the early phases of DLS development. This is necessary to manage resources prudently and guide the development of DLS.

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 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.

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 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.

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