AIMC Topic: Eye Diseases
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Applications of deep learning in fundus images: A review.
Medical image analysis
Jan 20, 2021
The use of fundus images for the early screening of eye diseases is of great clinical importance. Due to its powerful performance, deep learning is becoming more and more popular in related applications, such as lesion segmentation, biomarkers segmen...
A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability.
The Lancet. Digital health
Oct 1, 2020
Health data that are publicly available are valuable resources for digital health research. Several public datasets containing ophthalmological imaging have been frequently used in machine learning research; however, the total number of datasets cont...
Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective.
Progress in retinal and eye research
Sep 6, 2020
The simultaneous maturation of multiple digital and telecommunications technologies in 2020 has created an unprecedented opportunity for ophthalmology to adapt to new models of care using tele-health supported by digital innovations. These digital in...
Artificial Intelligence for Rapid Meta-Analysis: Case Study on Ocular Toxicity of Hydroxychloroquine.
Journal of medical Internet research
Aug 17, 2020
BACKGROUND: Rapid access to evidence is crucial in times of an evolving clinical crisis. To that end, we propose a novel approach to answer clinical queries, termed rapid meta-analysis (RMA). Unlike traditional meta-analysis, RMA balances a quick tim...
Advances in Telemedicine in Ophthalmology.
Seminars in ophthalmology
Jul 9, 2020
Telemedicine is the provision of healthcare-related services from a distance and is poised to move healthcare from the physician's office back into the patient's home. The field of ophthalmology is often at the forefront of technological advances in ...
Dense anatomical annotation of slit-lamp images improves the performance of deep learning for the diagnosis of ophthalmic disorders.
Nature biomedical engineering
Jun 22, 2020
The development of artificial intelligence algorithms typically demands abundant high-quality data. In medicine, the datasets that are required to train the algorithms are often collected for a single task, such as image-level classification. Here, w...
Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology.
The British journal of ophthalmology
Jun 12, 2020
With the advancement of computational power, refinement of learning algorithms and architectures, and availability of big data, artificial intelligence (AI) technology, particularly with machine learning and deep learning, is paving the way for 'inte...
A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations.
The Lancet. Digital health
May 12, 2020
BACKGROUND: Screening for chronic kidney disease is a challenge in community and primary care settings, even in high-income countries. We developed an artificial intelligence deep learning algorithm (DLA) to detect chronic kidney disease from retinal...
Microscopic Ophthalmic Surgery Using a Freely Movable Arm Support Robot: Basic Experiment and Clinical Experience.
Ophthalmic research
Feb 20, 2020
INTRODUCTION: The disadvantage of conventional armrests is the difficulty to adjust their height and position during surgery.
Automatic detection of rare pathologies in fundus photographs using few-shot learning.
Medical image analysis
Jan 28, 2020
In the last decades, large datasets of fundus photographs have been collected in diabetic retinopathy (DR) screening networks. Through deep learning, these datasets were used to train automatic detectors for DR and a few other frequent pathologies, w...
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