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Retinal Diseases

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Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis.

IEEE transactions on medical imaging
The automatic diagnosis of various retinal diseases from fundus images is important to support clinical decision-making. However, developing such automatic solutions is challenging due to the requirement of a large amount of human-annotated data. Rec...

Deep Learning for Automated Sorting of Retinal Photographs.

Ophthalmology. Retina
PURPOSE: Though the domain of big data and artificial intelligence in health care continues to evolve, there is a lack of systemic methods to improve data quality and streamline the preparation process. To address this, we aimed to develop an automat...

Exploring the effect of hypertension on retinal microvasculature using deep learning on East Asian population.

PloS one
Hypertension is the leading risk factor of cardiovascular disease and has profound effects on both the structure and function of the microvasculature. Abnormalities of the retinal vasculature may reflect the degree of microvascular damage due to hype...

Methodological Challenges of Deep Learning in Optical Coherence Tomography for Retinal Diseases: A Review.

Translational vision science & technology
Artificial intelligence (AI)-based automated classification and segmentation of optical coherence tomography (OCT) features have become increasingly popular. However, its 3-dimensional volumetric nature has made developing an algorithm that generaliz...

Domain-invariant interpretable fundus image quality assessment.

Medical image analysis
Objective and quantitative assessment of fundus image quality is essential for the diagnosis of retinal diseases. The major factors in fundus image quality assessment are image artifact, clarity, and field definition. Unfortunately, most of existing ...

Development and Evaluation of a Deep Learning System for Screening Retinal Hemorrhage Based on Ultra-Widefield Fundus Images.

Translational vision science & technology
PURPOSE: To develop and evaluate a deep learning (DL) system for retinal hemorrhage (RH) screening using ultra-widefield fundus (UWF) images.

AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images.

Medical & biological engineering & computing
Since introducing optical coherence tomography (OCT) technology for 2D eye imaging, it has become one of the most important and widely used imaging modalities for the noninvasive assessment of retinal eye diseases. Age-related macular degeneration (A...

Convolutional Neural Networks for Spectroscopic Analysis in Retinal Oximetry.

Scientific reports
Retinal oximetry is a non-invasive technique to investigate the hemodynamics, vasculature and health of the eye. Current techniques for retinal oximetry have been plagued by quantitatively inconsistent measurements and this has greatly limited their ...

Scale-space approximated convolutional neural networks for retinal vessel segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Retinal fundus images are widely used to diagnose retinal diseases and can potentially be used for early diagnosis and prevention of chronic vascular diseases and diabetes. While various automatic retinal vessel segmentation...

Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Spectral Domain Optical Coherence Tomography (SD-OCT) is a volumetric imaging technique that allows measuring patterns between layers such as small amounts of fluid. Since 2012, automatic medical image analysis performance ...