AIMC Topic: Retinal Diseases
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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.)
May 1, 2022
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
Apr 15, 2022
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...
A Systematic Review of Artificial Intelligence Applications Used for Inherited Retinal Disease Management.
Medicina (Kaunas, Lithuania)
Mar 31, 2022
Nowadays, Artificial Intelligence (AI) and its subfields, Machine Learning (ML) and Deep Learning (DL), are used for a variety of medical applications. It can help clinicians track the patient's illness cycle, assist with diagnosis, and offer appropr...
Detection of signs of disease in external photographs of the eyes via deep learning.
Nature biomedical engineering
Mar 29, 2022
Retinal fundus photographs can be used to detect a range of retinal conditions. Here we show that deep-learning models trained instead on external photographs of the eyes can be used to detect diabetic retinopathy (DR), diabetic macular oedema and po...
Development and quantitative assessment of deep learning-based image enhancement for optical coherence tomography.
BMC ophthalmology
Mar 26, 2022
PURPOSE: To develop a deep learning-based framework to improve the image quality of optical coherence tomography (OCT) and evaluate its image enhancement effect with the traditional image averaging method from a clinical perspective.
Deep learning-based classification of retinal vascular diseases using ultra-widefield colour fundus photographs.
BMJ open ophthalmology
Feb 4, 2022
OBJECTIVE: To assess the ability of a deep learning model to distinguish between diabetic retinopathy (DR), sickle cell retinopathy (SCR), retinal vein occlusions (RVOs) and healthy eyes using ultra-widefield colour fundus photography (UWF-CFP).
NGS and phenotypic ontology-based approaches increase the diagnostic yield in syndromic retinal diseases.
Human genetics
Aug 26, 2021
Syndromic retinal diseases (SRDs) are a group of complex inherited systemic disorders, with challenging molecular underpinnings and clinical management. Our main goal is to improve clinical and molecular SRDs diagnosis, by applying a structured pheno...
Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks.
Nature communications
Aug 10, 2021
Retinal 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 de...
Deep learning-enabled ultra-widefield retinal vessel segmentation with an automated quality-optimized angiographic phase selection tool.
Eye (London, England)
Aug 9, 2021
OBJECTIVES: To demonstrate the feasibility of a deep learning-based vascular segmentation tool for UWFA and evaluate its ability to automatically identify quality-optimized phase-specific images.
Diagnosis of retinal disorders from Optical Coherence Tomography images using CNN.
PloS one
Jul 27, 2021
An efficient automatic decision support system for detection of retinal disorders is important and is the need of the hour. Optical Coherence Tomography (OCT) is the current imaging modality for the early detection of retinal disorders non-invasively...
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