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Retinal Diseases

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Can artificial intelligence accelerate the diagnosis of inherited retinal diseases? Protocol for a data-only retrospective cohort study (Eye2Gene).

BMJ open
INTRODUCTION: Inherited retinal diseases (IRD) are a leading cause of visual impairment and blindness in the working age population. Mutations in over 300 genes have been found to be associated with IRDs and identifying the affected gene in patients ...

Enhancing retinal images in low-light conditions using semidecoupled decomposition.

Medical & biological engineering & computing
Eye diseases that are common and many diseases that result in visual ailments, such as diabetes and vascular disease, can be diagnosed through retinal imaging. The enhancement of retinal images often helps in diagnosing diseases related to retinal or...

Deep-learning-based AI for evaluating estimated nonperfusion areas requiring further examination in ultra-widefield fundus images.

Scientific reports
We herein propose a PraNet-based deep-learning model for estimating the size of non-perfusion area (NPA) in pseudo-color fundus photos from an ultra-wide-field (UWF) image. We trained the model with focal loss and weighted binary cross-entropy loss t...

Generative adversarial network-based deep learning approach in classification of retinal conditions with optical coherence tomography images.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: To determine whether a deep learning approach using generative adversarial networks (GANs) is beneficial for the classification of retinal conditions with Optical coherence tomography (OCT) images.

Systems medicine and artificial intelligence in retinal disease.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie

Temporal deep learning framework for retinopathy prediction in patients with type 1 diabetes.

Artificial intelligence in medicine
The adoption of electronic health records in hospitals has ensured the availability of large datasets that can be used to predict medical complications. The trajectories of patients in real-world settings are highly variable, making longitudinal data...

Multi-label classification of fundus images with graph convolutional network and LightGBM.

Computers in biology and medicine
Early detection and treatment of retinal disorders are critical for avoiding irreversible visual impairment. Given that patients in the clinical setting may have various types of retinal illness, the development of multi-label fundus disease detectio...

Robust Fovea Detection in Retinal OCT Imaging Using Deep Learning.

IEEE journal of biomedical and health informatics
The fovea centralis is an essential landmark in the retina where the photoreceptor layer is entirely composed of cones responsible for sharp, central vision. The localization of this anatomical landmark in optical coherence tomography (OCT) volumes i...

Validation of Soft Labels in Developing Deep Learning Algorithms for Detecting Lesions of Myopic Maculopathy From Optical Coherence Tomographic Images.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
PURPOSE: It is common for physicians to be uncertain when examining some images. Models trained with human uncertainty could be a help for physicians in diagnosing pathologic myopia.

Diagnosis of Retinal Diseases Based on Bayesian Optimization Deep Learning Network Using Optical Coherence Tomography Images.

Computational intelligence and neuroscience
Retinal abnormalities have emerged as a serious public health concern in recent years and can manifest gradually and without warning. These diseases can affect any part of the retina, causing vision impairment and indeed blindness in extreme cases. T...