Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs.
TLDR
A deep learning system can detect referable GON with high sensitivity and specificity and coexistence of high or pathologic myopia is the most common cause resulting in false-negative results.About:
This article is published in Ophthalmology.The article was published on 2018-03-02 and is currently open access. It has received 521 citations till now. The article focuses on the topics: Fundus (eye) & Optic disk.read more
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Artificial intelligence and deep learning in ophthalmology
Daniel Shu Wei Ting,Louis R. Pasquale,Lily Peng,John P. Campbell,Aaron Y. Lee,Rajiv Raman,Gavin Tan,Leopold Schmetterer,Pearse A. Keane,Tien Yin Wong +9 more
TL;DR: 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.
Journal ArticleDOI
Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography.
Kang Zhang,Xiaohong Liu,Jun Shen,Zhihuan Li,Ye Sang,Xingwang Wu,Yunfei Zha,Wenhua Liang,Chengdi Wang,Ke Wang,Linsen Ye,Ming Gao,Zhongguo Zhou,Liang Li,Jin Wang,Zehong Yang,Huimin Cai,Jie Xu,Lei Yang,Wenjia Cai,Wenqin Xu,Shaoxu Wu,Wei Zhang,Shanping Jiang,Lianghong Zheng,Xuan Zhang,Li Wang,Liu Lu,Jiaming Li,Haiping Yin,Winston Wang,Oulan Li,Charlotte Zhang,Liang Liang,Tao Wu,Ruiyun Deng,Kang Wei,Yong Zhou,Ting Chen,Johnson Y.N. Lau,Manson Fok,Jianxing He,Tianxin Lin,Weimin Li,Guangyu Wang +44 more
TL;DR: Using a large computed Tomography database from 4,154 patients, an AI system is developed that can diagnose NCP and differentiate it from other common pneumonia and normal controls and is made available globally to assist the clinicians to combat COVID-19.
Journal ArticleDOI
REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
José Ignacio Orlando,Huazhu Fu,João Barbossa Breda,Karel Van Keer,Deepti R. Bathula,Andres Diaz-Pinto,Ruogu Fang,Pheng-Ann Heng,Jeyoung Kim,Joon-Ho Lee,Joonseok Lee,Xiaoxiao Li,Peng Liu,Shuai Lu,Balamurali Murugesan,Valery Naranjo,Sai Samarth R. Phaye,Sharath M Shankaranarayana,Apoorva Sikka,Jaemin Son,Anton van den Hengel,Shujun Wang,Junyan Wu,Zifeng Wu,Guanghui Xu,Yongli Xu,Pengshuai Yin,Fei Li,Xiulan Zhang,Yanwu Xu,Hrvoje Bogunovic +30 more
TL;DR: It is observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task, and the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results.
Journal ArticleDOI
Deep learning-enabled medical computer vision.
Andre Esteva,Katherine Chou,Serena Yeung,Nikhil Naik,Ali Madani,Ali Mottaghi,Yun Liu,Eric J. Topol,Jeffrey Dean,Richard Socher +9 more
TL;DR: In this paper, the authors survey recent progress in the development of modern computer vision techniques-powered by deep learning-for medical applications, focusing on medical imaging, medical video, and clinical deployment.
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Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective
Ji Peng Olivia Li,Hanruo Liu,Darren Shu Jeng Ting,Sohee Jeon,R.V. Paul Chan,Judy E. Kim,Dawn A Sim,Peter B M Thomas,Haotian Lin,Youxin Chen,Taiji Sakomoto,Anat Loewenstein,Dennis S.C. Lam,Louis R. Pasquale,Tien Yin Wong,Linda A. Lam,Daniel S W Ting +16 more
TL;DR: 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, and this article reviews how countries across the world have utilised these digital innovations to tackle diabetes, retinopathy of prematurity, age-related macular degeneration, glaucoma, refractive error correction, cataract and other anterior segment disorders.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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Dropout: a simple way to prevent neural networks from overfitting
TL;DR: It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
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Rethinking the Inception Architecture for Computer Vision
TL;DR: This work is exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization.
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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
TL;DR: This work introduces a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrates that they are a strong candidate for unsupervised learning.
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Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
Varun Gulshan,Lily Peng,Marc Coram,Martin C. Stumpe,Derek Wu,Arunachalam Narayanaswamy,Subhashini Venugopalan,Kasumi Widner,Tom Madams,Jorge Cuadros,Ramasamy Kim,Rajiv Raman,Philip C. Nelson,Jessica L. Mega,Dale R. Webster +14 more
TL;DR: An algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy and diabetic macular edema in retinal fundus photographs from adults with diabetes.
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