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Diabetic retinopathy algorithms

Peter Kuzmak, Charles Demosthenes, April Maa
The US Department of Veterans Affairs has been acquiring store and forward digital diabetic retinopathy surveillance retinal fundus images for remote reading since 2007. There are 900+ retinal cameras at 756 acquisition sites. These images are manually read remotely at 134 sites. A total of 2.1 million studies have been performed in the teleretinal imaging program. The human workload for reading images is rapidly growing. It would be ideal to develop an automated computer algorithm that detects multiple eye diseases as this would help standardize interpretations and improve efficiency of the image readers...
December 3, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
Malvika Arya, Ramy Rashad, Osama Sorour, Eric M Moult, James G Fujimoto, Nadia K Waheed
Optical coherence tomography angiography (OCTA) is a noninvasive imaging modality for depth-resolved visualization of retinal vasculature. Angiographic data couples with structural data to generate a cube scan, from which en-face images of vasculature can be obtained at various axial positions. OCTA has expanded understanding of retinal vascular disorders and has primarily been used for qualitative analysis. Areas Covered: Recent studies have explored the quantitative properties of OCTA, which would allow for objective assessment and follow-up of retinal pathologies...
November 21, 2018: Expert Review of Medical Devices
Nadeem Salamat, Malik M Saad Missen, Aqsa Rashid
The diabetic retinopathy is the main reason of vision loss in people. Medical experts recognize some clinical, geometrical and haemodynamic features of diabetic retinopathy. These features include the blood vessel area, exudates, microaneurysm, hemorrhages and neovascularization, etc. In Computer Aided Diagnosis (CAD) systems, these features are detected in fundus images using computer vision techniques. In this paper, we review the methods of low, middle and high level vision for automatic detection and classification of diabetic retinopathy...
November 15, 2018: Artificial Intelligence in Medicine
Abdolreza Rashno, Dara D Koozekanani, Keshab K Parhi
Diagnosis and monitoring of retina diseases related to pathologies such as accumulated fluid can be performed using optical coherence tomography (OCT). OCT acquires a series of 2D slices (Bscans). This work presents a fully-automated method based on graph shortest path algorithms and convolutional neural network (CNN) to segment and detect three types of fluid including sub-retinal fluid (SRF), intra-retinal fluid (IRF) and pigment epithelium detachment (PED) in OCT Bscans of subjects with age-related macular degeneration (AMD) and retinal vein occlusion (RVO) or diabetic retinopathy...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Kang Zhou, Zaiwang Gu, Wen Liu, Weixin Luo, Jun Cheng, Shenghua Gao, Jiang Liu
Diabetic Retinopathy (DR) is a non-negligible eye disease among patients with Diabetes Mellitus, and automatic retinal image analysis algorithm for the DR screening is in high demand. Considering the resolution of retinal image is very high, where small pathological tissues can be detected only with large resolution image and large local receptive field are required to identify those late stage disease, but directly training a neural network with very deep architecture and high resolution image is both time computational expensive and difficult because of gradient vanishing/exploding problem, we propose a Multi-Cell architecture which gradually increases the depth of deep neural network and the resolution of input image, which both boosts the training time but also improves the classification accuracy...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Piotr Chudzik, Bashir Al-Diri, Francesco Caliva, Giovanni Ometto, Andrew Hunter
Diabetic retinopathy (DR) is an asymptotic complication of diabetes and the leading cause of preventable blindness in the working-age population. Early detection and treatment of DR is critical to avoid vision loss. Exudates are one of the earliest and most prevalent signs of DR. In this work, we propose a novel two-stage method for the detection and segmentation of exudates in fundus photographs. In the first stage, a fully convolutional neural network architecture is trained to segment exudates using small image patches...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
K A Iroshan, A D N De Zoysa, C L Warnapura, M A Wijesuriya, S Jayasinghe, N D Nanayakkara, A C De Silval
More than 8% of world population have diabetes which causes long term complications such as retinopathy, neuropathy, nephropathy and foot ulcers. Growing patient numbers has prompted large scale screening methods to detect early symptoms of diabetes (rather than elevated blood glucose levels which is a late symptom). Vascular tortuosity (twisted and curved nature of blood vessels) in retinal fundus images has proven to reflect the effect of diabetes on macrovasculature. However, large scale patient screening using retinal fundus images has limitations due to the requirement of a retinal camera...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Micael Pedrosa, Jorge Miguel Silva, João Figueira Silva, Sérgio Matos, Carlos Costa
BACKGROUND AND OBJECTIVE: Diabetic retinopathy (DR) is the most prevalent microvascular complication of diabetes mellitus and can lead to irreversible visual loss. Screening programs, based on retinal imaging techniques, are fundamental to detect the disease since the initial stages are asymptomatic. Most of these examinations reflect negative cases and many have poor image quality, representing an important inefficiency factor. The SCREEN-DR project aims to tackle this limitation, by researching and developing computer-aided methods for diabetic retinopathy detection...
December 2018: International Journal of Medical Informatics
Rajiv Raman, Sangeetha Srinivasan, Sunny Virmani, Sobha Sivaprasad, Chetan Rao, Ramachandran Rajalakshmi
Remarkable advances in biomedical research have led to the generation of large amounts of data. Using artificial intelligence, it has become possible to extract meaningful information from large volumes of data, in a shorter frame of time, with very less human interference. In effect, convolutional neural networks (a deep learning method) have been taught to recognize pathological lesions from images. Diabetes has high morbidity, with millions of people who need to be screened for diabetic retinopathy (DR)...
November 6, 2018: Eye
Minhaj Alam, Yue Zhang, Jennifer I Lim, Robison V P Chan, Min Yang, Xincheng Yao
PURPOSE: This study aims to characterize quantitative optical coherence tomography angiography (OCTA) features of nonproliferative diabetic retinopathy (NPDR) and to validate them for computer-aided NPDR staging. METHODS: One hundred and twenty OCTA images from 60 NPDR (mild, moderate, and severe stages) patients and 40 images from 20 control subjects were used for this study conducted in a tertiary, subspecialty, academic practice. Both eyes were photographed and all the OCTAs were 6 mm × 6 mm macular scans...
October 31, 2018: Retina
D Jude Hemanth, J Anitha, Le Hoang Son, Mamta Mittal
Disease diagnosis from medical images has become increasingly important in medical science. Abnormality identification in retinal images has become a challenging task in medical science. Effective machine learning and soft computing methods should be used to facilitate Diabetic Retinopathy Diagnosis from Retinal Images. Artificial Neural Networks are widely preferred for Diabetic Retinopathy Diagnosis from Retinal Images. It was observed that the conventional neural networks especially the Hopfield Neural Network (HNN) may be inaccurate due to the weight values are not adjusted in the training process...
October 31, 2018: Journal of Medical Systems
Clark H Stevenson, Sheng Chiong Hong, Kelechi C Ogbuehi
IMPORTANCE: Artificial intelligence (AI) algorithms are under development for use in diabetic retinopathy photo screening pathways. To be clinically acceptable, such systems must also be able to classify other fundus abnormalities and clinical features at the point of care. BACKGROUND: We aimed to develop an AI system that can detect several fundus pathologies and report relevant clinical features. DESIGN: Convolutional neural network training with retrospective data set...
October 29, 2018: Clinical & Experimental Ophthalmology
Hsin-Yi Tsao, Pei-Ying Chan, Emily Chia-Yu Su
BACKGROUND: The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated with the DR than others. If we can detect the DR related risk factors more accurately, we can then exercise early prevention strategies for diabetic retinopathy in the most high-risk population. The purpose of this study is to build a prediction model for the DR in type 2 diabetes mellitus using data mining techniques including the support vector machines, decision trees, artificial neural networks, and logistic regressions...
August 13, 2018: BMC Bioinformatics
Daniel Shu Wei Ting, Louis R Pasquale, Lily Peng, John Peter Campbell, Aaron Y Lee, Rajiv Raman, Gavin Siew Wei Tan, Leopold Schmetterer, Pearse A Keane, Tien Yin Wong
Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration...
October 25, 2018: British Journal of Ophthalmology
Alessandro Rabiolo, Francesco Gelormini, Riccardo Sacconi, Maria Vittoria Cicinelli, Giacinto Triolo, Paolo Bettin, Kouros Nouri-Mahdavi, Francesco Bandello, Giuseppe Querques
PURPOSE: To compare macular and peripapillary vessel density values calculated on optical coherence tomography angiography (OCT-A) images with different algorithms, elaborate conversion formula, and compare the ability to discriminate healthy from affected eyes. METHODS: Cross-sectional study of healthy subjects, patients with diabetic retinopathy, and glaucoma patients (44 eyes in each group). Vessel density in the macular superficial capillary plexus (SCP), deep capillary plexus (DCP), and the peripapillary radial capillary plexus (RCP) were calculated with seven previously published algorithms...
2018: PloS One
Zhixi Li, Stuart Keel, Chi Liu, Yifan He, Wei Meng, Jane Scheetz, Pei Ying Lee, Jonathan Shaw, Daniel Ting, Tien Wong, Hugh Taylor, Robert Chang, Mingguang He
OBJECTIVE: The goal of this study was to describe the development and validation of an artificial intelligence-based, deep learning algorithm (DLA) for the detection of referable diabetic retinopathy (DR). RESEARCH DESIGN AND METHODS: A DLA using a convolutional neural network was developed for automated detection of vision-threatening referable DR (preproliferative DR or worse, diabetic macular edema, or both). The DLA was tested by using a set of 106,244 nonstereoscopic retinal images...
October 1, 2018: Diabetes Care
Razieh Ganjee, Mohsen Ebrahimi Moghaddam, Ramin Nourinia
Diabetic retinopathy (DR) is one of the most complications of diabetes. It is a progressive disease leading to significant vision loss in the patients. Abnormal capillary nonperfusion (CNP) regions are one of the important characteristics of DR increasing with its progression. Therefore, automatic segmentation and quantification of abnormal CNP regions can be helpful to monitor the patient's treatment process. We propose an automatic method for segmentation of abnormal CNP regions on the superficial and deep capillary plexuses of optical coherence tomography angiography (OCTA) images using the marker-controlled watershed algorithm...
September 2018: Journal of Biomedical Optics
Zhe Jiang, Zekuan Yu, Shouxin Feng, Zhiyu Huang, Yahui Peng, Jianxin Guo, Qiushi Ren, Yanye Lu
BACKGROUND: Fundus fluorescein angiography (FFA) imaging is a standard diagnostic tool for many retinal diseases such as age-related macular degeneration and diabetic retinopathy. High-resolution FFA images facilitate the detection of small lesions such as microaneurysms, and other landmark changes, in the early stages; this can help an ophthalmologist improve a patient's cure rate. However, only low-resolution images are available in most clinical cases. Super-resolution (SR), which is a method to improve the resolution of an image, has been successfully employed for natural and remote sensing images...
September 19, 2018: Biomedical Engineering Online
R Karthikeyan, P Alli
Diabetic Retinopathy (DR) has been a leading cause of blindness in case of human beings falling between the ages of 20 and 74 years. This will have a major influence on both the patient and the society as it can normally influence the humans in their gainful years. An early DR detection is quite challenging as it may not be detected by humans. There are several techniques and algorithms that have been established for detecting the DR. These techniques have been facing problems to achieve effective sensitivity, accuracy, and specificity...
September 12, 2018: Journal of Medical Systems
Heba Radi AttaAllah, Asmaa Anwar Mohamed Mohamed, Mohamed Attia Ali
PURPOSE: The aim of this study was to evaluate macular perfusion using OCTA automated software algorithms; vessel area density (VD) and non-flow tool to measure FAZ area in treatment-naïve diabetic eyes with moderate or severe NPDR and having macular edema, and correlate these parameters with LogMAR (logarithm of the minimum angle of resolution) visual acuity. Diabetic eyes without macular edema were included, to detect and define differences within the parameters between diabetic eyes with and without macular edema...
September 7, 2018: International Ophthalmology
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